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The GeneSpring User Manual for version 4.1

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1. _ok _Het Figure 3 22 The List Inspector window Copyright 1998 2001 Silicon Genetics 3 45 Viewing Data in GeneSpring The Inspectors To use the Gene List Inspector Double click a gene list name in the navigator Or 1 Right click any gene list 2 Select Properties from the pop up menu To save the Gene List Inspector to a separate file click the Save to File button Selecta directory and file name and click Save To print your list click the Print List button Click OK To copy a list click the Copy to clipboard button Paste into a text editor To rename a list click the Rename List button Type in the new name and click OK You must also click OK in the main Gene List Inspector window to confirm your new name To use the Find Regulatory Sequences Function see Regulatory Sequences on page 4 26 for information about the Find Potential Regulatory Sequences Window Classification Inspector The Classification Inspector allows you to learn about the method used to construct a classifica tion or to learn more about the variability explained by each class within a classification To use the Classification Inspector right click a classification in the navigator panel and select the Inspect option Using the Classification Inspector Window In Figure 3 23 the notes field contains information about the method used to make the classifica tion If the classification is the result of clustering this fie
2. GeneSpring will not allow you to do this normalization and normalize to the median of each gene as they address the same issue Miscellaneous Regarding normalizing merged split experiments you have the option of starting with the orig inal normalizations or discarding these and starting with raw data The default setting starts you with the original normalizations To start with the raw data deselect the Start with nor malized data checkbox in the Experiment Normalizations window You can assign a minimum value for your measurements Any measurements that fall below this minimum value will be assigned the minimum value To assign a minimum value 1 Check the Make Minimum Value box under Miscellaneous 2 Enter a minimum value in the field to the right Global Error Models Using the Global Error Model The error model has changed significantly in GeneSpring 4 1 and now separate estimates of two different kinds of random variation are used to estimate the variability in gene expression mea surements e Measurement variation This comprises the lowest level of variation corresponding to the variation of the measurement of a gene on a single chip around the true value that would be achieved by a perfect measurement of the expression level of the gene for that sample e Sample to sample variation This is the variation between samples in the same condition This represents biological or sampling variability such as variab
3. The names of the positive and negative controls do not need to be listed in your Master Table of Genes If they are listed those genes will be colored gray not measured in the genome browser because they are used in normalization not measurement Once all your files are together you can start the Experiment Wizard Copyright 1998 2001 Silicon Genetics Appendix D 2 The Experiment Wizard The Experiment Import Wizard The Experiment Import Wizard Most of the panels in the Experiment Import Wizard are fairly self explanatory This section is mainly designed to show the different possible appearances a panel can have and add any notes about characteristics that are not obvious The Experiment Import Wizard saves your experiment information as an HTML file When you are entering a new experiment make sure the genome browser in the main GeneSpring window is displaying the genome the experiment refers to To initiate the Wizard selectFile gt Manual Load Experiment gt Experiment Import Wizard If you are about to load an experiment very similar to an experiment you already have in GeneSpring you can use the Experiment Import Wizard like this experiment to expedite the loading process In this case similar to means the same genome same file layout and similar conditions 1 The Welcome panel of the GeneSpring Experiment Entry Wizard will appear This panel will contain some instruction on how to prepare for using the wizard inc
4. To Show ORF direction Ignore ORF direction A gene is represented visually by a colored line or upon higher magnification a colored rectangle The rectangle s position relative to the chromosome line determines the direction of the ORF A gene below the chromosome line has a reading direction opposite to the direction chosen by the sequencers and the sequence is read backwards You can choose to display this distinction between which direction a gene is read Show ORF direction or to have no distinction between genes Ignore ORF direction To invoke either of these options Copyright 1998 2001 Silicon Genetics 3 13 Viewing Data in GeneSpring Physical Position View 1 Right click while the cursor is in the genome browser A menu will appear 2 Goto the Options submenu 3 Select the Ignore ORF direction command or the Show ORF direction command To Show complementary bases Just show one strand of bases Show complementary bases allows both of the complementary nucleotides to be shown while viewing the nucleic acid sequence in the physical position view Conversely Just show one strand of bases shuts this feature off and only views the Watson strand of the sequence To invoke either of these options 1 Right click while the cursor is in the genome browser A menu will appear 2 Select Options gt Just show one strand of bases or Show comple mentary bases Copyright 1998 2001 Silicon Genetics 3 14 Viewing Data in GeneSpri
5. Here is an example for a CLONTECH Array from a file Clontech layout Name Clontech 588 Icon XXX gif VerticalSubArrays 2 HorizontalSubArrays 3 HorizontalPerSubArray 14 VerticalPerSubArray 14 VerticalDuplication HorizontalDuplication 2 CommonArrayType Clontech Making an array is a complicated process please contact Silicon Genetics Technical Services Department at 650 367 9600 or support sigenetics com for more information on this topic Copyright 1998 2001 Silicon Genetics Appendix M 2 Technical Details on the Statistical Group Comparison For Each Gene Appendix N Technical Details on the Statistical Group Comparison Statistical Group Comparison is a filter tool that statistically compares mean expression levels between two or more groups of samples The object is to find the set of genes for which the spec ified comparison shows statistically significant differences in the mean normalized expression levels as interpreted according to your current interpretation mode logarithm ratio or fold change across all the groups This comparison is performed for each gene and the genes with the most significant differential expression smallest p value are returned The comparisons can be done with parametric or non parametric methods The parametric comparison for two groups is known as Student s two sample t test For multiple groups this is known as one way analysis
6. Ifyou might be loading files of this format in the future click Remember this format Load Wow Remember this Format Cancel Figure 2 1 The Column Editor GeneSpring will have guessed which row represents your column titles If GeneSpring is incor rect click the Column Titles cell at the far left Use the Move Headline Up or Move 2 2 Copyright 1998 2001 Silicon Genetics Creating DataObjects in GeneSpring The Experiment Autoloader Headline Down buttons to select a new row to use as column titles If your file has no column titles deselect the check box marked Has column titles 1 Inthe row marked Function you can assign functions to each column Choose a function from the pull down menu in each column See Figure 2 1 You can have unlimited Flag and Unas signed columns however other functions can only be used once At least one Gene Name col umn and one Signal raw data column are required e Ifyou assign a Flag column you will be able to specify the letter or number indicating Present Absent and Marginal calls 2 After your initial assignments click the Guess the Rest button and GeneSpring will attempt to label the remaining columns If GeneSpring is incorrect click the Clear Guess button to remove the column labels 3 Ifyou wish to use the same format in the future select Remember This Format This format will be added to the cache of recognized formats and GeneSpring will suggest it in the future No
7. 2AndromedaA40 txt Experimetn36FileName 2AndromedaBO txt Experiment37FileName 2AndromedaB10 txt Experiment38FileName 2AndromedaB20 txt Experiment39FileName 2AndromedaB30 txt Experiment40FileName 2AndromedaB40 txt Data File Header Lines If you have more than one data file and they have different column layouts then you must answer these questions for every experiment sample data file you have 11 Does your data file have one or more headlines not containing experimental data Headlines number of headlines in the data file Headlines 1 If your data files all use different layouts but all of them have the same number of headlines you may use the general object name given above rather than entering the number of headlines for each data file If you have more than one data file with different numbers of headlines use the object name given below If you are doing this make sure to indicate the number of headlines for every sample Experiment Headlines number of headlines in the data file of the experiment indicated ExperimentlHeadlines 1 Experiment2Headlines 3 Experimetn3Headlines 1 Appendix J 7 Copyright 1998 2001 Silicon Genetics Installing from a Text File Gene Names Gene Names 12 Which column of your data file contains the gene name GeneColumn number of the column the gene name is found in GeneColumn 1 If your data files all have a different column layout but all of them have the gen
8. 3 Click OK There are six categories you can change e High Expression High expression refers to the normalized expression of your genes it is the vertical axis of the color bar The default for this is 6 0 e Normal Expression For most normalization procedures in GeneSpring the data will be nor malized to 1 0 The default for this is 1 0 e Low Expression For most normalization procedures in GeneSpring the data will not have negative numbers The default for this is 0 0 e High Control High control refers to the control strength of your genes It is represented by the horizontal axis of the colorbar The default for this is 200 0 e Medium Control tThe default for this is 100 0 Low Control tThe default for this is 50 0 For example you could change the usual range of an experiment to high 10 normal 5 and low 2 resulting in a very different color scheme once you click OK There is no Edit gt Undo Ctr1 2Z function for this type of change If you want to return to your previous coloration scheme you must re open the Experiment Data Range pop up window and type in your old values For more details on trust please see Trust on page 3 32 For more details on normalization please see Normalizing Options on page G 1 Copyright 1998 2001 Silicon Genetics 3 36 Viewing Data in GeneSpring The Inspectors Changing the Default Colors You can change the colors used by GeneSpring to display the g
9. Global Error Models on page 2 26 For information about creating a resizable picture see Saving Pictures and Printing on page 6 2 For information on bookmarks see Bookmarks on page 3 31 Gene Inspector Tools The box in the bottom left corner of the Gene Inspector contains tools allowing you to search for genes having similar expression profiles to the gene being displayed in the Gene Inspector e Find Similar Allows you to search for genes with similar expression profiles to the gene being inspected Each gene expression profile must have the required minimum correlation to be considered similar The higher the minimum correlation maximum 1 the closer the gene expression profiles have to be Enter this number in the Minimum correlation box above the Find Similar button For information on using the Find Similar function see Making Lists with the Find Similar Command on page 4 13 e Complex Correlation Allows you to make a gene list comparing the gene being inspected to genes having similar expression profiles in multiple experiments with more complex parameters than the Find Similar tool allows For information on using the Complex Correla tion function see Making Lists with the Complex Correlation Command on page 4 14 e Save As Drawn Gene Allows you to save your gene expression profile as a drawn gene which you can use to make lists For information on making lists from drawn genes see Cre atin
10. SansSerif Monospaced and Dialog Please be aware some virtual machines support the use of explicit names for fonts that are available to the operating sys tem e The Unique ID prefix field allows users to specify an alphanumeric prefix that will be appended to the identifier field within data files If you commonly share genelist files between different GeneSpring installations it is a good idea to give each installation dif ferent ID prefix so GeneSpring is not confused by genelists with similar identifiers The Your Name Your Group Name and Your Email fields contain the text that is con tained in the HTML files that go into your data directories Appendix B 5 Copyright 1998 2001 Preferences Window The Miscellaneous Appendix B 6 Copyright 1998 2001 Genome Wizard Appendix C Genome Wizard Each and every genome known to GeneSpring must have its own genomedef file You can create a genomedef file by hand please refer to The genomedef File on page I 1 by using the Auto loader please refer to Creating a Genome through the Autoloader on page 2 7 or by using the Genome Wizard The Genome Wizard will guide you through the steps of creating a genomedef file Most of these panels are fairly self explanatory Most Wizard panels will take up most of your screen This is to prevent any necessary boxes from being shrunk to a non visible size You can change the size of any panel in the usual manner of
11. Test 5 Additional Random Starting Clusters V Discard genes with no data for half the conditions Start Close Figure 5 5 The GeneSpring Clustering window 2 Choose a gene list from the Gene List folder in the navigator right click the list and select Set Gene List To remove a gene list select the list in the Genes to Use box and click Remove e To add restrictions to the selected list right click an experiment or gene list in the naviga tor and select a restriction For information on restrictions and how to apply them see Filtering Genes on page 4 1 e Selecting Discard Genes With No Data For Half The Conditions dis cards any genes with no data in at least half the conditions in the selected experiment 3 To add an experiment or condition click on an experiment or condition in the Experiments folder of the navigator Enter a weight in the pop up window Click the Add button under Experiments to Use To remove an experiment or condition select the experiment or condition under Experiments to Use and click Remove Copyright 1998 2001 Silicon Genetics 5 10 Clustering and Characterizing Data in GeneSpring k Means Clustering e The weight of the condition is a measure of the influence the condition has on the correla tion distance e g an experiment with a weight of 2 0 will be twice as influential as one with a weight of 1 0 4 Enter the Number of Clusters that you wish to make 5 Choose the max
12. The Bottom Buttons Across the bottom of the Experiment Inspector are several buttons Data Range The Data Range button will bring up the Data Range window You can use this window to alter what measurements are considered high normal or low in GeneSpring s coloration scheme Any changes made in this window will be saved and affect your experi ment when you click OK For more information about the Data Range and how it affects the color your experiment is presented in the main GeneSpring window please refer to Changing the Experimental Data Range on page 3 36 Attachments The Attachments button brings up an Attachments window You can add any number of files or folders to your experiment from this window Any changes made in this window will be saved when you click Close View File The View File button will launch your default browser and allow you to view all of the information associated with your experiment in HTML format OK This button will save all your data Cancel This button will close the Experiment Inspector window without saving any of the changes you may have made in any of the white text boxes Condition Inspector A condition a unique combination of parameters as applied to your sample Each condition may be a single sample or a group of replicate samples combined based upon the parameter values defined for each sample The easiest way to think of this is as the parameters under which the sample s was ob
13. There are several scripts already in your GeneSpring program You cannot delete these scripts You can select the various building blocks to make a script For a really long or intensive script you may one to make several little scripts and them join them together Inputs Inputs can go only to one place Input will appear at the top of the screen as icon identifying lists genome or other dataobject Inputs will be joined from item to item by lines these lines are thin lines for only one item and thick lines for groups Blue lines indicate a valid pathway red lines indicate a possible problem details will be given at the bottom of the screen Knobs Knobs are user defined variables Look in the basic knobs section on the right middle of the win dow for drop down menus of options frequently the type of data to be used see Data Types for Restrictions on page 4 7 This allows for greater flexibility as you can define whatever you need at the moment for the script to function Outputs Multiple outputs are acceptable to GeneSpring but if there are many new windows resulting from your script you may see a warning message before the are displayed Outputs can be displayed in GeneSpring or saved automatically to GeNet If there is no output in your current script there will be a warning line at the bottom of the window Saving your Scripts When you are done and no more error or warning essages appear you can save your script by cl
14. ayout file for Region Specifications ccceeccesceeeeeteeeteeeeeeees J 9 Locate the Data Columa epes a teat tee ees it at se cae ea atl J 9 The Control Channel Value assis cotter cde te eases gacshcoate des bana dutewaulecanacbugcesmienessasaes J 11 INIGAS IRENE TEARS isu dce tances aane u Bathe ionic oil ana oa ar sid and J 12 Associating a Picture with a Sample 00 ccccccccccsccessecsteeseceescecseceeeseeeesseecaeeneeeees J 13 Normalizations Negative Controls 1 23 cs sicsseeisssccdssnssscessbesscevek sosacasstcctanseaes J 14 The required layout file for negative controls oo ee eeceeeseeeteceeeeeeeeeseeneeeeees J 15 Normalizations Control Channel Values c ccccccccceseceseceeseeeeeeeeeceeeeeeeeeeseeeaeees J 15 6 Copyright 2000 2001 Silicon Genetics Normalizations Positive Controls ccccccccceesssessssssssssssssssessesssssssnsnsssssssssnsnsnssseeees J 16 The required layout file for positive controls cceecceesceceseceeeeeeeeeeeeeeeenteeeees J 16 Normalizations Each Sample to Itself c sccscc dssecdeadencedeocsiad cvacssentedaseecdssbanaeuneesye J 17 Normalizations Each Gene to Itself sy i025 acts esspctuhoivesuen sat sevdds aalealewtoaananeies J 18 Normalizations Each Sample to a Specific Sample 00 0 0 cccceccceesseeseeeeeeeeneeeneeeees J 18 Colorbar Specifications lt 4 cosvangsiat castes soak ween eas shad bree J 19 Graph Specification S x aches piel patti e A A lait R J 19 Appendix K
15. e Paste Transposed Allows you to copy a row from a tab delimited text file or spreadsheet and paste it into a column e Clear Clears selected cell e Replace Allows you to replace many entries at once Select the entries you wish to change and choose Replace Copyright 1998 2001 Silicon Genetics 2 8 Creating DataObjects in GeneSpring Change Experiment Parameters Or to replace all instances of an entry choose Replace and then deselect the Replace in selected cells only checkbox before clicking OK Extract Sub values This feature automates parameter assignment To use it you must create file names based on your parameter values e g Rlr001a txt where Rlr0 refer to an experi ment and 01 is your sample number and a is the region designator When you implement the Extract Sub values feature file names are broken down into sub values GeneSpring is programmed to first look for alternating constant fields and variable fields and to make parameters out of the variable fields Next it divides the variable fields into groups consist ing of uninterrupted stretches of either numbers letters or non alpha numeric characters and makes parameters out of each of these groups Fill Down Allows you to replace entries using the top selected cell Click on the cell you would like to use as the replacement and then holding down the Shift key click on the cells underneath whose values you would like replaced with the original
16. on page 1 Other helpful files might include e Layout file e Region designation files e A file listing the positive controls e A file listing the negative controls e GIF or JPEG pictures to be associated with this experiment or with particular samples within the experiment e GIF or JPEG pictures of the Microarray plates the experiment was done on Raw Data An experimental file consists of a list of gene names a list of the raw data associated with them and the condition or conditions during the test In addition an experiment may involve more than one sample various normalization controls such as positive and negative controls and control channel values pictures of the conditions during the experiment and pictures of the array plates the experiments were done upon Appendix K 1 Copyright 1998 2001 Silicon Genetics Experiment File Formats What format does this data need to be in What format does this data need to be in Data may be in any of the following eight formats depending on the type of data represented Experimental Data You will need at least one file containing your experimental data This file must have the gene names listed in one column one name per line with the experimental data reported in columns If it were viewed in a spreadsheet it might look like this Gene Contro Canol Background Background Signal for Experiment Name Sirens Channel Signal the Referenc
17. 5 sets 25 3 6434124 6 junclassified 1 al AllClasses 597 2 540684 Percent explained variability 60 642 x Figure 3 23 Classification Inspector for a k means clustering with 5 groups References for the Classification Inspector Calinski T and Harabasz J 1974 A dendrite method for cluster analysis Communications in Statistics 3 1 27 Gordon A D Classification 2nd Ed Monographs on Statistics and Applied Probability 82 Chapman amp Hall CRC Boca Raton 1999 Copyright 1998 2001 Silicon Genetics 3 48 Analyzing Data in GeneSpring Filter Genes Analysis Tools Chapter 4 Analyzing Data in GeneSpring Filter Genes Analysis Tools The Filter Genes Analysis tools allow you to take a current gene list and apply a series of restric tions or filters to make a smaller list These restrictions can pertain to an entire experiment or interpretation or to a single condition or sample The filters include factors such as quality con trol control strength expression level constraints sample to sample fold comparison statistical group comparisons and associated numbers restrictions All restrictions applied to create a new list are saved as an attachment to the new list The ability to restrict a gene list based on the behavior of its genes in experiments or in individual samples is an important quality control tool You may want to remove genes with low precision large error values those that do not vary significan
18. Color by Venn Dia gram on page 3 33 for more details e Use on Scatter Plot This option will give you two selectable items Vertical Axis and Hori zontal Axis You can assign data from this list as one or the other e Delete List Selecting this will result in a caution window asking you to verify the deletion of the list Click Yes to delete e Inspect This command brings up the Inspect Gene List window where you can view many details about the history and contents of your list Please refer to List Inspector on page 3 44 for more details The Experiment Subfolder Pop up Menus A right click over an experiment will bring up the following commands e Display Primary Experiment Selecting this option will reset the genome browser to show that experiment It is quicker to just select the experiment through the navigator with a left click e Set Secondary Experiment This will add the secondary experiment to the genome browser e Inspect This will bring up a window with the administrative information associated with this experiment You can click the Edit button to change most of the information presented in the Inspect window Appendix P 9 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Experiment Specifica tion area Delete Experiment Selecting this will result in a caution window asking you to verify the deletion of the experiment Click Yes to delete Delete Experiment Interpretation
19. Despite the name of the window you can save the result either as a classification or as gene lists by selecting one of the two Save Classification as radiobuttons Select a name for your classification list and click Save Viewing k means clusters If you use k means clustering to produce a classification you can get details about the classifica tion in the Classification Inspector For information about the Classification Inspector see Clas sification Inspector on page 3 46 Perhaps the easiest way to view a classification is with the Split Window feature Right click a classification or a gene list created with k means clustering and select Split Window gt Both The genome browser will divide into several smaller displays You can also choose verti cally or horizontally Copyright 1998 2001 Silicon Genetics 5 11 Clustering and Characterizing Data in GeneSpring Self Organizing Maps Self Organizing Maps The self organizing map SOM is a clustering technique similar to k means clustering but SOMs in addition to dividing genes into groups based on expression patterns illustrate the rela tionship between groups by arranging them in a two dimensional map SOMs are useful for visu alizing the number of distinct expression patterns in your data and determining which of these patterns are variants of one another SOMs were invented by Tuevo Kohonen 1991 2000 and are used to analyze many kinds of data Applications to gen
20. Figure 3 1 Zooming To return directly to the unmagnified state do one of the following e Select the View gt Zoom Fully Out option e TypeCtrl Home Panning If you have zoomed in and need to view genes that are not visible in the genome browser but are nearby you can pan in any direction To pan do one of the following e Use the arrow keys to move in the desired direction e Use the Page Up or Page Down keys to travel one screen s distance up or down Copyright 1998 2001 Silicon Genetics 3 1 Viewing Data in GeneSpring Using Genome Browser Changing Genome Browser Elements To change genome browser elements right click on the genome browser to select any of the fol lowing items in the Options submenu e Change Vertical Axis Range Allows you to change the range of the vertical axis e Show Hide Timeline Allows you to show or hide the timeline e Show Hide Horizontal Label Allows you to show or hide the label on the horizontal axis e Show Hide Vertical Label Allows you to show or hide the label on the vertical axis e Label Vertical Axis at Top Label Vertical Axis on Side Gives you the option of placing the vertical axis label on the side of the vertical axis or on top e Show Hide Experiment Name Allows you to show or hide the experiment name in the top right corner Error Bars You have the option of using error bars in the Graph and Scatter Plot views To turn the error bars on right click in the
21. How far up the tree you have to go to find a branch containing both organisms can be considered a measure of how different the organisms are You can classify genes in a similar manner clustering those whose expression patterns are similar into nearby places in a tree Such mock phylogenetic trees are often referred to as dendro grams GeneSpring can both create and display such trees GeneSpring can also create trees of experi ments displaying the genes along the X axis and the samples along the Y axis This can be exceedingly powerful for many applications for example seeing if any environmental stressors cause similar effects on the expression levels as mutant organisms do If you have already created or downloaded trees open the Gene Trees folder in the navigator and select any tree for viewing Creating a New Gene Tree For detailed instructions on creating a Gene Tree in GeneSpring with the default values please refer to GeneSpring Basics Instructional Manual Chapter 6 Trees on page 6 1 While viewing any list 1 In the main GeneSpring screen select Tools gt Clustering 2 In the Clustering window select Make New Tree from the drop down list labeled Cluster ing Method 3 Select the Start button at the bottom of the screen This will start the process of computing and annotating a gene tree As this is a computationally intensive process it could take a few minutes A Clustering Progress bar will indicate the prog
22. Self Organizing Maps Third Edition Springer Verlag Berlin Tamayo P Slonim D Mesirov J Zhu Q Kitareewan S Dmitrovsky E Lander E Golub T 1999 Interpreting patterns of gene expression with self organizing maps Methods and appli cation to hematopoietic differentiation Proc Nat Acad Sci USA 96 2907 2912 Copyright 1998 2001 Silicon Genetics 5 14 Clustering and Characterizing Data in GeneSpring The Class Predictor The Class Predictor The Class Predictor is designed to predict the value or class of an individual parameter in an uncharacterized sample or set of samples It does this in two steps First the Class Predictor algo rithm examines all genes in the training set individually and ranks them on their power to discrim inate each class from all the others Next it uses the most predictive genes to classify the test set i e the set where the parameter value of interest is unknown For example you could attempt to diagnose the leukemia type of a leukemia patient with the Class Predictor by using expression data from patients whose leukemia type was known You can also use the Class Predictor simply to find genes whose behavior is related to a given parameter by examining the list of predictor genes The list of predictor genes is assembled by ordering all the measurements for a given gene accord ing to their normalized expression levels For each class parameter value the predictor pla
23. THIS EXPERIMEN y time 10 minutes time 20 minutes time 30 minutes time 40 minutes time 50 minutes LABELS time 60 minutes time 70 minutes time 80 minutes time 100 minutes time 110 minutes l time 120 minutes time 130 minutes 30 nno GBD xM time 140 minutes iets time 160 minutes Figure 3 8 Tree View with annotations The genome browser in Figure 3 8 is displaying a gene tree The genes are the colored rectangles down at the bottom joined to each other by green lines As there are over six thousand vertical green lines in this view of the yeast genome they tend to blur into each other producing a solid green bar Similarly colored genes tend to be clustered together as expected This will hold true for different points in the experiment You can see the color changes vertically as the current con tinuous parameter is arranged down the right side Copyright 1998 2001 Silicon Genetics 3 17 Viewing Data in GeneSpring Tree View Magnifying Trees The magnification in the Tree View is not quite the same as in the other views due to the need to keep the genes in the view along with the immediate tree branches The amount of magnification will be visible in the parameter specification area just below the genome browser Selecting and Viewing Subtrees 1 Zoom inas described in GeneSpring Basics Instructional Manual 6 1 3 Zooming in on the Tree View on page 6 7 2 Select any node by clicking over its intersec
24. Type your identification into the screen as necessary and click the Upload button When uploading genomes to GeNet there is an Update Existing Genome checkbox under your password This field is always unselected by default Normally if you try to upload a genome which is already present on the server it simply gives an error message If you select this option by clicking in the box GeNet will update the genome to make it like the genome you are uploading Specifically GeNet will e add new genes to the genome e change annotations on existing genes e change the lists of hypertext links for genes and experiments However GeNet will not remove genes from the genome since there might be gene lists experi ments etc which involve those genes Copyright 1998 2001 Silicon Genetics 6 7 Exporting GeneSpring Data Publish to GeNet Using GeNet To view your data or someone else s on GeNet you will need to start your usual web browser and go to the web page specified by your administrator Enter your user GeNet ID and password to log on Select a genome to view and click Continue Loading Data from GeNet You can download data objects from GeNet and manipulate them on your local copy of Gene Spring 1 From the main GeneSpring window select File gt Load Data from GeNet You will be prompted for your GeNet user name and password 2 Type in your GeNet user name and password Click OK A window may appear informing you GeneSprin
25. as discrete points When a non continuous element is graphed each parameter value is placed on the horizontal axis in order from left to right GeneSpring will automatically order numerical parameters from highest to lowest and order non numerical parameters in alphabetical order See Re order the Parameters on page 2 10 if you wish non numerical parameter values to be graphed in a particular non alphabetical order When displaying data from a non continuous parameter data points are graphed in histograms as discrete points A gene deletion is a simple example of a non continuous element but it is by no means the only possible non continuous parameter A non continuous parameter is occasionally referred to as a set when there are other parameter display options employed especially when a continuous parameter is used because the non continuous parameter separates the data into a series of discrete graphs viewed next to each other on the same screen When a continuous param eter is used in conjunction with a non continuous parameter each discrete graph contains all of the parameter values of the continuous parameter making each of the separate graphs look like a set of parameter values Color Code A color code is used for experimental parameters whose parameter values exist independently of one another but are not unrelated to one another When the genome browser is colored by param eter GeneSpring will order the parameters value
26. branchiness of the tree e Default minimum distance is 0 001 A value smaller than 001 has very little effect because most genes are not correlated more closely To change default minimum distance number move the cursor into the white box next to the Minimum distance label and click in the box then use the keyboard to alter the text just like using a word processing program You will not normally want to modify the minimum dis tance References for Hierarchical Clustering Everitt Brian S Cluster Analysis 3rd Ed Arnold London 1993 pp 62 65 Eisen Michael B et al Cluster analysis and display of genome wide expression patterns Proc Natl Acad Sci USA V95 pp 14863 14868 December 1998 Copyright 1998 2001 Silicon Genetics 5 4 Clustering and Characterizing Data in GeneSpring Principal Components Analysis Principal Components Analysis Principal components analysis PCA is a decomposition technique that produces a set expression patterns known as principal components Linear combinations of these patterns can be assembled to represent the behavior of all of the genes in a given data set It should be noted that PCA is not a clustering technique Rather it is a tool to characterize the most abundant themes or building blocks that reoccur in many genes in your experiment To perform a PCA analysis select Tools gt Principal Components Analysis Principal Components Analysis E E ioj x The window
27. expression values for each point in N dimensional space where N is the number of experimental points conditions with data in your experiment and the expression profile for gene B This is more formally known as the Euclidian metric To standardize this difference GeneSpring divides by the square root of the number of conditions This is how to compute a Euclidian Distance Distance a b square root of N Since distance is a measure of dissimilarity the distance d is converted when needed to a simi larity measure 1 1 d Special Case Correlations The next three metrics should only be used to look at special cases They are all modified versions of the Standard correlation Using these three metrics only makes sense when your data is in a sequence such as before and after a time series or a drug series The sequence does not have to be continuous but it must have an order If your experiment is set up with an experimental point taken at each of before after and control then the following correlations will not make sense applied to your data Smooth Correlation This is how to compute a Smooth correlation Make a new vector A from a by interpolating the average of each consecutive pair of elements of a Insert his new value between the old values Do this for each pair of elements that would be connected by a line in the graph screen Do the same to make a vector B from b Smooth correlation
28. fold Within sample variability is calculated in terms of normalized ratio expression and translated as necessary to the interpretation mode by use of the delta method Copyright 1998 2001 Silicon Genetics 2 28 Creating DataObjects in GeneSpring Global Error Models Results of the variance components analysis are used to estimate standard deviations and standard errors according to the grouping of samples into conditions as specified by the experiment inter pretation Two different types of interpretation affect the assumed context of the calculation e Single sample interpretation If all conditions contain only one sample for instance the All Samples interpretation precision calculations are based solely on the estimated within sample measurement variation The error bars standard deviations and standard errors repre sent the variability of all possible measurements on this specific sample e Multi sample interpretation If at least one condition contains multiple samples precision calculations for all samples are based on the combined within sample and between sample variation and error bars standard deviations and standard errors represent the variation of measurements of samples representing the population of all possible samples in the condition In a multi sample interpretation if no replicate samples are available for a specific condition then no error calculations are made and no error bars are shown since there i
29. instrumentation has already calculated the ratio of the signals The Use control chan nel for trust function tells GeneSpring to use the control channel to determine the saturation of the color of your genes 2 Inthe Use values over box enter the value below which you do not trust the control sig nal values below this cut off will be thrown out Per chip Normalizations You will usually want to perform a per chip normalization which controls for chip wide varia tions in intensity This variation could be due to inconsistent washing inconsistent sample prepa ration or other microarray production or microfluidics imperfections GeneSpring will not allow you to perform more than one per chip normalization as they all address the same issue If you have flags assigned to your data select which data you would like used in your per chip normalization from the Use genes marked pull down menu Use Positive Control Genes Some chips come with positive controls mRNA from another genome or housekeeping genes which are used to control for differences in the amount of exposure between samples The for mula for this difference is signal strength of gene A in sample X median signal of the positive controls in sample X To use Positive Control Genes 1 Create a separate positive control file by listing the names of your positive controls in the first column of a spreadsheet and saving in tab delimited text format 2 Under Per
30. on page 2 17 Step 4 Annotate your genome optional Most researchers will want to import the maximum amount of biological information avail able about each gene before beginning analyses After collecting the data it is a good idea to make lists of genes based on appropriate keywords a SelectAnnotations gt GeneSpider b Select a database from which to update your annotations Then select the column in your master gene table that contains the accession number usually Column 10 for the GenBank locus Make sure there are accession numbers in the column you select c Click the Start button the GeneSpider may continue gathering information for many hours Remember to click Save and close when the GeneSpider is finished For details on the GeneSpider see Annotation Tools on page 2 15 At this point you are ready to begin working with your data Copyright 1998 2001 Silicon Genetics 1 10 Introduction Basic actions GeneSpring Basics Once you have loaded your data GeneSpring will open a window with information from your new genome and initially display all the genes in your experiment If you just opened Gene Spring and want to see your new genome select File gt Open Genome or Array and choose your genome from the pop up list TOOLS AND FEATURES ARE ACCESSED THROUGH THE PULL DOWN gt GeneSpring 4 1 Yeast Gene Lists Experiments Gene Trees Experiment Trees Classifications Pathways A
31. when the global error model is not turned on or should not be used in the analysis e Parametric test global error model variances filters based on the variances estimated by the global error model If the global error model is not turned on this test is equivalent to the Parametric test don t assume variances are equal option e Non Parametric test checkbox filters based on the rank of each sample rather than the expression level Non parametric comparisons use the Wilcoxon two sample rank test also known as the Mann Whitney U test for two groups and the Kruskal Wallis test for multiple groups This test will be most successful if you have more than five replicate samples in each group 4 Selecta minimum P value cutoff for genes that pass the filter Select a type of multiple testing correction There are five options that are described below Multiple Testing Corrections When testing the statistical significance of group comparisons for many genes if you rely on the nominal p value many genes will pass the filter by chance alone For instance if you test 10 000 genes for reliable changes between groups at significance level 0 05 then assuming the tests are independent you would expect to misidentify about 500 genes as significant even when there is no real difference gene expression Even if you identify 1 000 genes showing significant behavior by this approach half of the genes on the list will have appeared by chance whi
32. 0 05 or 5 are shown Random Rate The intrinsic probability which is the percent of genes you would expect this specific nucleotide combination to appear upstream of if the nucleotide sequence were strictly random it is not of course but this is a good value to compare the observed probability to Observed Other Genes The observed probability of this sequence motif appearing upstream of genes other than the list under inspection If the option Relative to sequence upstream of other genes is selected this becomes the probability of the observed sequence occurring relative to the genes not in the list i e relative to the all genes list If the option Relative to whole genomic sequence is selected this becomes the probability of one or more occurrences of the sequence based on the rate of occurrence in the entire genome The formula used to calculate this is Ld where k the number of occurrences in the whole sequence b the total number of bases n the length of the upstream region being searched Expected The number of incidences in the searched gene list that you would expect this oligomer to occur The number for the Expected column is derived using the larger of the intrinsic probability and the observed probability values Single P this column gives the Single P value for the motif This is the chance this partic ular sequence would be found if only one test was performed Tests The number of tests run to
33. 1998 2001 Silicon Genetics 6 3 Exporting GeneSpring Data Exporting Gene Lists out of GeneSpring 4 Choose options on the Copy Annotated Gene List window by checking or unchecking the boxes 5 Click the Copy to Clipboard button 6 Paste the list into another application To save an annotated gene list 1 Select a gene list from the Gene List folder in the navigator 2 SelectEdit gt Copy gt Copy Annotated Gene List A menu will appear 3 Choose the experiment interpretation from the Copy based on interpretation pull down menu See Changing the Experiment Interpretation on page 2 17 for information on experiment interpretations 4 Click the Save to Disk button 5 Choose a name and location to save your gene list The resulting text file can be opened in any program that accepts tab deliminated text such as spreadsheet and word processing programs Annotation Options Your options for copying and saving information with an annotated gene list are listed in the Copy Annotated Gene List window Descriptions of these items can be found by clicking the Help but ton The type and amount of information listed will vary depending on your genome and the way that genome was loaded into GeneSpring e Gene List Associated Value The values if any that GeneSpring has associated with this gene list This column will only show up if you have associated values Refer to Adding an Associated Number Restriction on page 4 9
34. 2 Name your interpretation and click Save to overwrite your current interpretation or Save As to create a new interpretation Copyright 1998 2001 Silicon Genetics 2 17 Creating DataObjects in GeneSpring Changing the Experiment Interpretation You will find saved interpretations by clicking on the relevant experiment in the Experiments folder of the navigator You can delete an interpretation you have created by right clicking over it in the navigator and selecting Delete from the pop up menu Vertical Axis Modes The default display is Ratio where normalized intensity values are graphed on the vertical axis In this mode values range from zero to infinity 5 04Normalized Yeast cell cycle time series no 90 min 4 0 3 0 2 0 time minutes o 10 20 30 40 50 60 70 80 100 110 120 130 140 150 469 Figure 2 3 The gene list like CLN1 graphed using the signal control formula The Y axis is graphed from 0 to 5 The ratio is determined by dividing the signal raw data by the control strength In a one color experiment the control strength refer to the denominator used to normalize the raw data in a two color experiment it is the control channel When data is reported as the signal divided by the con trol it is assumed that all expression values are positive The number 1 is considered normal expression any expression value above one is overexpressed and all underexpressed data is less than one but greater than zero Th
35. 20 ParameterlExperiment4 30 ParameterlExperiment5 40 ParameterlExperiment6 0 ParameterlExperiment7 10 ParameterlExperiments8 20 ParameterlExperiment9 30 ParameterlExperimentl10 40 ParameterlExperimentll 0 ParameterlExperiment1l2 10 Parameter2Experiment A Parameter2Experiment2 A Appendix J 4 Copyright 1998 2001 Silicon Genetics Installing from a Text File Appendix J 5 Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param Param eter2Experiment3 A eter2Experiment4 A eter2Experimentd A eter2Experiment6 B eter2Experiment7 B eter2Experiment B eter2Experiment9 B eter2Experiment10 B eter2Experimentl1 A eter2Experiment12 A eter3Experiment Test eter3Experiment2 Test eter3Experiment Test eter3Experiment4 Test eter3Experiment Test eter3Experiment6 Test eter3Experiment7 Test eter3Experiments8 Test eter3Experiment9 Test eter3Experiment10 Test eter3Experiment1ll Test eter3Experimentl12 Test eter3Experiment13 Test eter3Experimentl14 Test eter3Experimentl15 Test eter3Experimentl6 Test eter3Experimentl7 Test eter3Experiment18 Test ete
36. 3 46 Printing Pictures 6 2 Trees with labels 3 18 Properties of Experiment D 4 J 1 Protein Product H 3 Publish to GeNet 6 7 P value 4 11 R raw data K 1 References Values see Control Channel Val ues region designation file K 6 Region Normalization D 8 G 15 multiple arrays J 9 Regulatory Sequence 4 26 Expected 4 28 Observed 4 28 P value 4 28 Random Rate 4 28 Copyright 1998 2001 Silicon Genetics Sequence 4 28 Single P 4 28 Tests 4 28 rename gene list 3 46 replicate parameter J 4 restrict data types Control Signal 4 7 Normalized Data 4 7 Number of Replicates 4 7 Range of Normalized Data 4 8 Raw Data 4 7 Standard Deviation 4 8 Standard Error 4 8 T test probability 4 8 restricting data types 4 8 RT PCR Experiments D 12 S Sample Photos D 11 J 13 Save List 3 46 Scatter Plot view 3 15 color by secondary experiment 3 35 Scripts 4 32 Secondary Animation Controls 3 6 Secondary Picture 3 6 select a gene s 3 4 P 1 deselect a gene 4 22 Self Organizing Maps P 4 Separation ratio 5 3 5 4 SGD H 2 gene list formats H 2 Show All 3 6 Show complementary bases P 6 similarity definitions see also correlations Smooth correlation 4 16 L 4 SOM Euclidean distance 5 13 Spearman Confidence 4 17 L 3 Spearman correlation 4 17 L 3 Split Window 3 30 classification 3 35 SQL E 2 Standard correlation 4 16 L 2 Standard deviation error bar P 7 Syntax G 10 Systematic Name H 2 Index 5 T Table of Genes see Master Gene Table Tool
37. A B A B Appendix L 4 Copyright 1998 2001 Silicon Genetics Equations for Correlations and other Similarity Measures Special Case Correlations Change Correlation The Change correlation looks for the opposite of what the Smooth correlation looks for The change correlation only looks at the change in expression level of adjacent points However it is also very similar to the Standard correlation in that it measures the angular separation of expres sion vectors for genes A and B around zero i e in comparison to zero except instead of using the expression values in each experimental point to create the expression vector for gene A it is based on an arc tangent transformation of the ratio between adjacent pairs of experimental points and uses these to create the expression vector This correlation looks for when gene A and gene B are changing at the same time Using the arc tangent makes a measure of change that is less sensi tive to outliers than using the ratio directly This is how to compute a Change correlation Make a new vector A from a by looking at the change between each pair of elements of a Do this for each pair of elements that would be connected by a line in the graph screen The value created between two values a and a is atan a a 7 4 Do the same to make a vector B from b Change correlation A B A B Upregulated Correlation The Upregulated correlation is very similar to the Change correlation except
38. Appendix G 9 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalizing All Samples to Specific Samples Normalizing All Samples to Specific Samples This normalization option is intended to remove differing intensity scales from each sample by comparing all of the samples to one or more specific samples The formula for this is the signal strength of gene A in sample X the signal strength of gene A in the control sample s Do not use this normalization method in concert with normalizing each gene to itself or normaliz ing to control channel values as they are all intended to address the same issue Unless your experiment was designed with specific control samples it is recommended you normalize each gene to itself i e to the median across all samples rather than using this normalization method Only use this normalization if you have control samples for which you consider the measurements very reliable and you want all of the measurements for the other samples to be in relation to those very reliable samples You will need normalization definitions for a your samples before you begin this Required Syntax for Normalization to Specific Samples In this scenario you will need to use a very specific syntax to describe your samples If you are normalizing to a single sample indicate the sample number in the box labeled Enter Sample Number s If you wish to normalize all of your samples to the mean of a set o
39. Do this for each pair of elements that would be connected by a line in the graph screen The value created between two values a and a is max atan a a 7 4 0 Do the same to make a vector B from b Result A B A B Copyright 1998 2001 Silicon Genetics 4 16 Analyzing Data in GeneSpring Making Lists with the Complex Correlation Com mand e Pearson Correlation Calculate the mean of all elements in vector a Then subtract that value from each element in a Call the resulting vector A Do the same for b to make a vector B Result A B A B e Distance Distance is not a correlation at all but a measurement of dissimilarity Distance is the measurement of Euclidian distance between the expression profile for gene A defined by its expression values for each point in N dimensional space where N is the number of condi tions with data in your experiment and the expression profile for gene B Result a b divided by the square root of the number of conditions with data e Spearman Correlation Order all the elements of vector a Use this order to assign a rank to each element of a Make a new vector a where the i element in a is the rank of a in a Now make a vector A from a in the same way as A was made from a in the Pearson Correlation Similarly make a vector B from b Result A B A B e Spearman Confidence Compute a value r of the spearman correlation as described above Result 1 probability you would get
40. E A T E E ca uevcuceemiusresssnueeces 1 1 8 Copyright 2000 2001 Silicon Genetics Introduction Chapter 1 Introduction Welcome to GeneSpring Congratulations on selecting the most advanced flexible tool available for gene expression data analysis This manual is a guide to GeneSpring features To see the many features new to version 4 1 see New in Version 4 0 on page 1 4 Chapter 1 will cover installing GeneSpring loading and set ting up your data and GeneSpring basics The remaining chapters will discuss loading set up and the various data analysis and visualization tools in detail Getting Started Requirements e A computer with 128 MB RAM 256 MB strongly recommended with a Pentium II Celeron PowerPC or faster processor e Approximately 130 MB including documentation e The recommended screen resolution is 1024x768 with a minimum of 16 bit color Installing from a CD If you are installing GeneSpring from a CD you will see several options after you place your CD in the drive 1 Select Install GeneSpring Demo A splash screen and an Install Anywhere screen will appear with a progress bar 2 Follow the on screen instructions For more information see the ReadMe file included with the CD In Windows you can also install the software by using the Start gt Run command in the Start menu Installing from the Web If you are reading this manual and do not have a copy of GeneSpring you can download a c
41. EE a tl 2 13 Non Continuous Element Set c ccccceccsscccsscccssececssececsseceesseeeeseeecseeeeseeeens 2 13 Color Codere tera tea aeaa a acta a A N AEE 2 13 ATIMOTATION TOOLS astisen ararte mennina AE EE e ESE AE EEEE RAEES A AESi kaien 2 15 Updating your Master Gene Table with GeneSpider 0 0 0 0 eecceceesceesteeeteeee 2 15 Building a Simplified Ontology fscscncscus esis orate sense iaeias waar huietiees 2 16 Changing the Experiment Interpretation c csccs ccsscoaseesesstvesteatoauceeb vsacentedsccsssaisonss 2 17 Vertical Axis Modes nnne a a EE E E a A A E ERE ai 2 18 Param ter Display Mod s s3 i 5salissseneesntet aves na ne cis uaicteiaea sae a a 2 20 Experiment NormaliZations a icsicstes sce a teeiens Vedas tae eech dete terse aaah atienevees 2 21 Back erounid SUD acti Oph smsni E E e AT AAAA RTEA EE A 2 21 Per spot Normalization es tas cate detec caisde Goeedatecinacaleaeus mou eenat uaa cute cetdceuaelenesct wes aeunctles 2 22 1 Copyright 2000 2001 Silicon Genetics Per Chip NOMMALZALIONS ax cccs ccsecatetadcta Ais cass tea nd Gust teens E uae EREE 2 22 Use Positive Control Genes sitrer aieea E EAE EAEE EEA 2 22 Normalizing to the Distribution of All Genes ssessesssssessesessessesesseseesessersessesee 2 23 Region Normalization noeneen raaa Ea E T A ta tet 2 23 The Affine Background Correction cccccecssecsssceseceeeceesceceseceeeeeeeeeseecnaeeees 2 23 ise Constant Values srei a ceo ieee Ca aD oat 2 24 Per
42. Gene List window will appear with a list of similar genes and lists 3 Name your list and click Save Your new list will appear in the genome browser and in your Gene Lists folder Pathways A pathway is a graphical representation of the interaction between gene products in a biological system Genes can be superimposed on the pathway allowing you to view their expression levels in a biological context You can zoom in on a pathway and move the slider to watch gene expres sion change over the experimental conditions You can draw pathways yourself or use publicly available pathways such as KEGG Kyoto Ency clopedia of Genes and Genomes One scenario in which a pathway can be very useful is if you are trying to identify a class of genes that are associated with a particular step or regulatory ele ment within a pathway gt GeneSpring 4 1 Yeast Genes all genomic elements ia lol x Fie Edit Yiew Experiments Colorbar Tools Annotations Window Help Figure 4 4 A Pathway cyclin and other genes during Metaphase of the cell cycle Copyright 1998 2001 Silicon Genetics 4 23 Analyzing Data in GeneSpring Pathways In Figure 4 4 at about 20 minutes you can see that the genes believed to be involved in S phase are overexpressed colored in red Importing a Pathway You can find pathways on the Web at sites such as e KEGG at ftp kegg genome ad jp pathways e BioCarta at www biocarta com e SPAD Signaling Pathway Databas
43. Genetics The Experiment Wizard The Experiment Import Wizard 26 27 28 The Normalizations Each Gene to Itself panel will appear In this panel you tell GeneSpring if you want to normalize each gene to itself so the median of all of the measurements taken for the gene is 1 If you are not doing a two color experiment you generally want to do this so the default setting for this panel is to perform this normalization If you do not wish to employ this normalization select No radio button in the first question Ifyou wish to use this normalization there is a second question Sometimes something will go wrong with the experiments and all of the values for a particular gene are very low in which case it will artificially inflate the noise of the gene if you normalize those values up to a median of 1 Specify this cut off by entering a number in the Enter lower median cut off value box The default setting for the cut off value is 0 01 Normalizing each gene to itself is optimal for more than five samples as with less than five the display becomes unintuitive Generally the better option for five samples or less is to do normalization against a particular sample For a mathematical illustration of this normalizing option please refer to Normalizing Each Gene to Itself on page G 8 The Normalizations All Samples to Specific Samples panel will appear This panel tells GeneSpring if you want to normalize each sample in the exper
44. H Experiments Choose from Genes all genes Make List 6 308 of 6 308 genes pass restrictions and then 117 pass correlations Correlations pe weight 1 0 Yeast cell cycle time series no 90 min Default Interpretation Remove Cumulative Distribution 6 147 Maximum 1 0 Minimum 0 95 Standard Correlation x Restrictions Remove i Cancel Help Figure 4 2 The Multi Experiment Correlation window Copyright 1998 2001 Silicon Genetics 4 15 Analyzing Data in GeneSpring Making Lists with the Complex Correlation Com mand The Correlations box Below the Gene List box is the Correlations box On the left of the Correlations box is a white box indicating the experiments chosen to correlate against the gene listed in the title bar The experiments selected may be weighted making one more important than another If both experiments chosen are given a weight of 1 they will be averaged equally The name of the experiment is noted directly after its relative weight The equa tion used to determine the overall correlation is X Aa Bb Cc a b cr e A is the correlation coefficient between the gene in question in experiment 1 and the gene named in the title bar of the Multi Experiment Correlation window also from experiment 1 e ais the weight specified for experiment 1 e Bis the correlation coefficient of the gene in question in experiment 2 to the gene name
45. In this example genes 1 5 and 9 are all marked as in region A and could be normalized as a dis crete group An Example You have experiment 1 with subchips A B C Da Dd 2 repeats for subchip D to be compared to experiment 2 with subchips A B Ca Cb D 2 repeats for subchip C You can load it as four samples Exp 1 A B C Da Exp 2 Db Exp 3 A B Ca D Exp 4 Cb Table A 1 Correct entry of repeated sub experiments Give experiments and 2 the same parameters Give experiments 3 and 4 the same parameters Entering region specifications when they are not specified in their own column or as suffixes within another column Appendix K 5 Copyright 1998 2001 Silicon Genetics Experiment File Formats What format does this data need to be in Occasionally a region may not be designated by a unique column entry or as a suffix appended to a column entry In this case you cannot use the Experiment Wizard to automatically read in your region designations You will need to create a layout file for your experiment and separate region designation files A region designation file is used to describe a region and specifies the follow ing information e How to distinguish this region from other regions e How to map gene names in this region to the gene names given in the list of genes defining the genome There are several ways regions can be distinguished The four ways listed below are typically used
46. Lists g all genes H Experiments Gene Trees xp11 4 xp11 3 xp11 23 HJ Experiment Tre E Classifications H Pathways EH Array Layouts U01337 PKS2 H Drawn Genes External Progr 1H Bookmarks E Scripts M25269 ELK1 30 0n x mMm Ypter yp11 32 vp11 31 Trust 0 Animate Horiz mag 12 0 Vert mag 20 Zoom Out E EET CN em a Figure 3 6 Zooming in for a closer look at the Y chromosome At high magnifications the labels associated with the chromosome s cytogenetic bands are dis played Copyright 1998 2001 Silicon Genetics 3 12 Viewing Data in GeneSpring Physical Position View To use the Load Sequence command In GeneSpring versions 4 0 and later the default setting of the program is to load the sequence information if available If you have an old version of GeneSpring and cannot update it please refer to Update GeneSpring on page A 2 please follow these directions The Load Sequence command is only applicable for sequenced organisms Loading the nucleic acid sequence allows you to magnify a section of the physical position view to the point where the nucleic acid sequence is displayed Loading the sequence also allows you to take advantage of GeneSpring s other sequence based features such as Tools gt Find Potential Regula tory Sequences Loading the nucleic acid sequence can be done in a number of ways Method 1 takes immediate effect 1 Right click while the cursor is in
47. O 2 Appendix P Common Commands sesssessoossoossosssssesssesssoossoosssosssoeessesssoossosssossssesssoese P 1 Commands Accessible by Cursor or Keyboard sssessseessesessssessessessressessessrssressesse P 1 Common Commands in the Drop Down menus s ssssssessesessssessessrssressessrssresseeseese P 2 The Rille Men inr nirna aye dea e e a a E E aiees Gbtueetoee P 2 Th Edit Menu esaigun aen u aren e E E R it P 2 Th eView M i e a a a AA aE AA AAAA AA AEEA P 3 The Experiments WCIt 5 2 sisesais4sdcceychsecacstahsecseasnieccc a ae a a a P 3 Th Col rbar Men sr roston ai e E A AE O E AR A a P 3 Phe Fools Mentem aae eae e a e A a a a P 4 Common Commands in the Genome Browser s sesessessessesessssessessresressessrssresseeseese P 5 The Options SUbmen 4c lt cctats aise ane duatutalinea ais a a e a P 5 The Error Bars Submenu s 4c 2Assceds Aiasestaiacnseivea eta nte Aideewo i a Aad P 7 Common Commands in the Navigator csseeidscstaxtcvessuadtaadhshdscaupaeiasdssandesaiaeddcaslanionias P 7 The Main Folder Pop up Menus sc lt cccccseinesscsisctoaaseteedssntocerestvintedesdectaisabccteantuees P 8 The Gene Lists Folders Pop up Menus 5 3 5 5 04505 lt ds sisedsscesteaacesaewts calseeadeiadaatestdvaecs P 8 Common Commands in the Experiment Specification area sssesessesseseesseseeses ee P 10 Appendix O GLOSSARY ia siiienccstaasitossiatiussansacecansedasiecscduncdocateassncoinissealesiecesveteavietensneveaenis Q 1 PINON EE
48. Output is a Group of Gene Trees Filter Number Group For each Boolean in the first argument pass through the corre sponding Number if the Boolean is true 1 Boolean Group input amp 1 Number Group input Output is a Group of Numbers Filter Sequence Group For each Boolean in the first argument pass through the corre sponding Sequence if the Boolean is true 1 Boolean Group input amp 1 Sequence Group input Output is a Group of Sequences 9 Look Up Number associated with gene in Condition Return the number 0 if none associated with a gene in a condition 1 Gene input amp 1 Condition input Knob for Type Output is a Number Number associated with gene in Gene List Return the number 0 if none associated with a Gene in a Gene List 1 Gene input amp 1 Gene List input Output is a Number See if Gene List contains a gene Return True if a Gene List contains a given Gene 1 Gene input amp 1 Gene List input Output is a Boolean 10 Numbers Compare 1 number Compare a number to another number specified as a parameter 1 Number input Knobs for Comparison amp Number Output is a Boolean Compare 2 numbers Compares two numbers 2 Number inputs Knob for Compari son Output is a Boolean Number Produce the number specified in the parameter Knob for Number Output is a Number Number Add Add two numbers together 2 Number inputs Output is a number Number Div Divide the first number by the
49. P 5 Ordered List view Interesting Genes 4 21 ORF direction Ignore P 6 Show P 6 orthologous genes 4 31 over expressed color changing B 2 P Panning 3 1 parameter numeric F 2 Parameter Characteristics D 5 J 2 Parameter Interpretations fold change 100 is 1 50 is 1 2 19 log ratio 2 18 ratio 2 18 ratio of signal control 2 18 Parameter names J 2 Parameter Values D 5 J 4 Parameters category J 3 color code J 3 continuous J 3 discrete J 4 display D 5 display instructions J 2 non continuous J 4 non numeric 2 10 2 13 F 2 numbers J 2 numeric 2 10 2 13 order 2 10 replicate J 4 set J 4 units J 2 Pass Fail column see Flags pasting data D 3 Pathway view 3 23 4 23 adding new elements 4 24 multiple genes 4 24 PCA see Principal Components Analysis Pearson correlation 4 17 L 2 Percent Explained variability 3 47 phase offset 4 18 Phenotype H 3 phylogenetic tree see Tree View Index 4 Physical Position view 3 10 commands 3 13 Picture 3 6 Pictures J 13 Positive Controls J 16 minimum average J 17 Predictor 5 15 Preferences window B 1 background color B 3 color B 2 data directory B 1 Database B 1 Default Correlation B 5 Default Font B 5 default genome B 1 Desired Memory B 5 Disk Cache Size B 5 firewall B 4 GeNet Address B 5 License Manager B 5 Restrict Gene List Searches B 5 selected color B 3 structure color B 3 Unique ID prefix B 5 web browser defaults B 4 Principal Components Analysis 5 5 P 4 Print List
50. R i dans Siesta ips aes aegis 3 8 Cl ssifications View sks vss iiare aaae aa aa aa a EAE RTEA AAE AEAT a ai hiaat 3 9 Physical Position Vi W seesnicseniobesiisnsiiioiinnrcs inisini ano isens iiec 3 10 Scatter Plot View norima aa a E e TA AR EAN i 3 15 IEA AE EA TREE AE R A EAEE EE 3 17 Magnifying tees srce ngndi A E R gy a 3 18 Selecting and Viewing Subtrees one aied eda uhaiiaa denoted 3 18 Viewing Nodes ma ada a E Mace skeet ada Aten Baa ona ds 3 18 Viewing Gene Names in Trees 2 3 0 ssesascaaadanzaesssdansdsdodhateeacebteadstlorndesdateess 3 19 Viewing Colors in Trees sdecscnniircnio nri aeaii i a i 3 19 Viewing Parameters in Trees ssessssseessesseeseesseesrssresseeseesresseessesressteseesersseesee 3 19 Horizontal Genes Vertical Genes 4 12 4csscustsnesnrarsesducd oksazssdedeeceasvaenttaataanmtaeeede 3 20 Ordered List View cotin en iii a taiate ai toa aeaa aaa naa aa 3 21 Atray Layout VIG We en l niran ranen ere dinate A E G aS 3 22 Pathway VIEW minene e E n e E e a E 3 23 Compar Genes to GeneSisni hann a a E E E ER a etal 3 24 Graph by Genes VC W asiccsciiicsnisiicistissianei seoseis iioipois eainiie ai sesia iseis in aes 3 26 Functional Classification eesesseseesseessessesseessessesseesseosossoesseessesessesseosossoesseosessesses 3 27 Viewas Spreadsheet csore aeaa AE ETE RESA A AARTE ARE 3 29 Linked Wid Ow onesna natai eaea e E AE A E EEEa 3 30 SHI WV O WS Meitsi ae e a A A O a E ANEGA 3 30 OO TVA E CRRA A A EA E A 3
51. See the Finding and Selecting Genes on page 3 4 for how to select genes See Making Lists from Selected Genes on page 4 22 for more details on this method of making a gene list e Making Lists from Conjectured Regulatory Sequences Once you have found possible regulatory sequences using the Find Potential Regulatory Sequences window see Regula Copyright 1998 2001 Silicon Genetics 1 17 Introduction Commonly Used GeneSpring Functions tory Sequences on page 4 26 for more details and are inspecting one of the sequences in the Conjectured Regulatory Sequence window you can make a list of all of the genes containing that sequence by selecting List gt Make Gene List See Using the Conjectured Regu latory Sequence window on page 4 29 for more information Copyright 1998 2001 Silicon Genetics 1 18 Creating DataObjects in GeneSpring The Experiment Autoloader Chapter 2 Creating DataObjects in GeneSpring The Experiment Autoloader The Experiment Autoloader is a time saving feature that is programmed to automatically recog nize and load most data formats The Autoloader automatically recognizes the following formats e Clontech AtlasImage 2 0 e Affymetrix Metrics e Affymetrix Pivot e Axon GenePix 4000 e BioDiscovery Imagene 4 e Incyte Internet e Incyte GEM Tools 2 4 e Packard Biochip GSI Lumonics ScanArray e Packard Biochip QuantArray 4000 e Generic one color e Generic two color If the Autoloa
52. Silicon Genetics 2 9 Creating DataObjects in GeneSpring Change Experiment Parameters Add a Parameter Click the Add Parameter button at the bottom of the window and a new column will appear at the far left You can paste in columns of information by clicking the cells of the Sample section For example if you had an Excel spreadsheet of data and wanted to copy and paste a column from it you could copy a large section of column and paste it into the new column You can also copy information out You can only add columns parameters and parameter values you cannot add rows sam ples into this table Re order the Parameters To change the order of your parameters as they are displayed in along the X axis in the main GeneSpring window you will need to select an entire column or part of a column and then use the Set Value Order button at the very bottom of this panel Sort Descending For example if you wanted to show the numeric continuous parameter Kryptonite Concentra tion in reverse order 40 30 20 10 0 of the normal arrangement 0 10 20 30 40 you first need to change the setting to a non numeric parameter and select the column by clicking on the gray bell at the very top You cannot change the order of a parameter defined as numeric To select part of a column you can highlight it in the normal fashion or while holding down the Shift key click in the top most cell you want GeneSpring will select down the column
53. Tutorial Go to Help gt Tutorial Help buttons on GeneSpring Clicking a Help button in a given window in GeneSpring opens windows a page explaining the features of that window Technical support Call Silicon Genetics toll free at 1 866 SIG SOFT 7638 Copyright 1998 2001 Silicon Genetics 1 3 Introduction New in Version 4 0 New in Version 4 0 Scripting GeneSpring 4 1 can execute scripts to automate data analysis Users connected to GeNet have the option of running scripts on a remote server Easier Data Loading With just a few clicks of the mouse Gene Spring s new Autoloader makes every attempt to recog nize the format of your file and the genome to which it corresponds If the Autoloader is unfamil iar with your file format you can use the Column Editor to specify the type of data in each column Once the Column Editor learns the location and identity of the relevant columns of data it adds these specifications to its list of known file types so that you can load subsequent experi ments in batch The Autoloader now automatically recognizes the following formats e Clontech one color e Clontech two color e Quantarray e Scanarray4000 e Affymetrix Metrixs e Affymetrix Pivot e Axon GenePix 4000 e BioDiscovery Imagene 4 e Incyte Internet e Incyte GEM Tools 2 4 e Generic one color e Generic two color Simplified Gene Ontology Construction The Build Simplified Ontology option constructs a simple g
54. UniGene requires that you choose an organism from the pull down menu e g human rat mouse zebrafish cow or frog 5 Click Start to begin updating annotations Copyright 1998 2001 Silicon Genetics 2 15 Creating DataObjects in GeneSpring Annotation Tools Building a Simplified Ontology New to GeneSpring 4 1 is the Build Simplified Ontology function which builds a gene ontology list based on the Gene Ontology Consortium classifications GeneSpring builds a hierarchical list from data found in all fields of the master gene table The Build Simplified Ontology function places over 300 biologically meaningful groups in lists that can be compared and merged By using these Gene Ontology lists you can study expression patterns of specific categories of genes by simply browsing through them Note You cannot rename these gene lists but you can update them To build a Simplified Gene Ontology list 1 SelectAnnotations gt Build Simplified Ontology 2 Name your folder 3 Click OK You will find your new Simplified Ontology list in the Gene Lists folder To make Gene Lists From Properties To create lists based on annotations see Making Lists from Properties on page 4 19 Copyright 1998 2001 Silicon Genetics 2 16 Creating DataObjects in GeneSpring Changing the Experiment Interpretation Changing the Experiment Interpretation The Change Experiment Interpretation window allows you to determine how an experiment is to be
55. a list of all the genes that are 2 fold over expressed or 2 fold under expressed in at least 1 condition in the input experiment and send all the results to GeNet e Best k means This script tries a K means classification with 3 5 8 and 15 clusters and choose the one with the highest explained variability e Select k means This script tries 2 k means with user input number of clusters and choose the k means classification with the highest explained variability Typically the scripts will divide you data into groups such a samples or conditions and perform analysis on these groups sets A group can be gene lists or conditions Scripts create and process groups You can create many groups possibly more than GeneSpring can handle at one time The Script Inspector Within GeneSpring you can right click over any script and select Inspect to examine that par ticular script In the Script Inspector you can edit the notes and history of your script Using the Remote Server For computational intensive scripts it is recommended you use the remote server option This will send your data to a remote computer and allow you to keep working speedily at your local computer Copyright 1998 2001 Silicon Genetics 4 33 Analyzing Data in GeneSpring Creating Your own Scripts Creating Your own Scripts The first step will be purchasing and installing the Script Editor Once the Script editor is installed just click on the icon on the desktop
56. a value of r or higher by chance e Two sided Spearman Confidence Compute a value r of the spearman correlation as described above Result 1 probability you would get a value of r or higher or r or lower by chance The Restrictions box The bottom white box is labeled Restrictions In it are the restrictions the genes have to pass before they reach the correlation stage The possible restrictions are discussed in detail in Filter ing Genes on page 4 1 Creating and Saving Your Correlated List The Make List command makes a list but does not close the Multi Experiment Correlation win dow The OK button at the bottom of the window makes a list and closes the Multi Experiment Correlation window The Cancel button also at the bottom of the window simply closes the Multi Experiment Correlation window Type in a unique name for your new list in the Name box and click OK Copyright 1998 2001 Silicon Genetics 4 17 Analyzing Data in GeneSpring Finding Offset Genes Finding Offset Genes In GeneSpring you can find genes whose profiles are similar to a specific gene but are offset by one or more conditions 1 Start from the Gene Inspector window Zoom in on any gene using the Edit gt Find Gene and double click or Ctrl I 2 Click the Complex Correlations button in the lower left corner of the window For details about the other elements in the Gene Inspector window please refer to Gene Inspector on page
57. all genes using 50th percentile cutoff 10 e Options Use background correction if necessary anything but absent e Per gene Median for each gene cutoff 0 01 if 2 samples Two Color Experiments Two color experiments are automatically normalized to a signal ratio Two color normalizations will automatically display all information flagged as Present or Unknown e Per spot Use control channel to calculate ratio cutoff 10 e Per chip Distribution of all genes using 50th percentile cutoff 0 01 e Options Use background correction if necessary anything but absent Default Normalizations of Commercially Available Products Affymetrix Pivot Table will automatically display all information flagged as Present or Unknown e Per chip Distribution of all genes using 50th percentile cutoff 10 e Options Use background correction if necessary anything but absent e Per gene Median for each gene cutoff 0 01 if 2 samples By default GeneSpring forces negative values to zero Metrics will automatically display all information flagged as Present or Unknown e Per chip Distribution of all genes using 50th percentile cutoff 10 e Options Use background correction if necessary anything but absent e Per gene Median for each gene cutoff 0 01 if 2 samples By default GeneSpring forces negative values to zero Axon GenePix 4000 will automatically display all information flagged as Present or Unknown e Per spot Use
58. ally your gene lists will be ordered so that the associated values appear in descending order If you do not have associated values your genes will appear in the same order as in the Master Gene Table To select a gene in the Graph by Genes view you must use the Edit gt Find Gene command Clicking directly on the experiment line will not produce any results Copyright 1998 2001 Silicon Genetics 3 26 Viewing Data in GeneSpring Functional Classification Functional Classification It is possible to display genes according to some classification system The Classification View is the usual way to display unsequenced organisms Generally the classification can come from either proprietary data which has assigned a label to each gene or it can come from a set of lists such as the Gene Onology lists already in the Gene Lists folder of the default yeast genome You can also create classifications using GeneSpring s various features Coloring According to a Folder of Lists As an example these are the instructions to create a classification view with the Gene Ontology Lists 1 Select View gt Classification You will see an unsorted classification 2 Open the Gene Lists folder in the navigator Open the Gene Ontology subfolder Position the cursor over the biological process lists subfolder and click the right button get ting a pop up menu The command Use as Classification will be at the top 3 SelectUse as Classification op
59. an ODBC driver specific to the type of database you will be using Appendix E 1 Copyright 1998 2001 Silicon Genetics Installing from a Database Custom Databases and GeneSpring Structured Query Language Structured Query Language SQL is a standard language for defining and accessing relational databases All of the major database servers used in client server applications work with SQL It is a query language designed to extract organize and update information in relational databases Each database vendor has its own particular dialect These dialects are similar to one another but different enough that programmers must pay close attention to which RDBMS is being used The most important dialects of SQL are ANSI ISO SQL IBM DB2 SQL Server Oracle Ingres and ODBC SQL uses statements to get work done Examples of some of these statements are e SELECT e INSERT e DELETE e UPDATE e DECLARE e OPEN e CLOSE e CREATE e PREPARE e DESCRIBE SQL Call Level Interfaces When a Call Level Interfaces CLI is used a program requests database services by calling spe cial SQL interface routines rather than embedding SQL statements directly into the program There are two distinct types of CLIs First each DBMS vendor provides its own unique API for its database The vendor specific API is usually the most efficient way to access the database but each vendor s API is unique As a result if you decide to write programs that use a v
60. and Control to display the mean of the raw and control signals e Max of Raw and Control to display the higher of the raw or control signal Right click the vertical axis select the Vertical Axis Mode submenu and choose an option as in step 4 You can further modify the appearance of the plot by right clicking the genome browser and selecting one of the following from the Options submenu e Show Lines or Hide Lines to add or remove the diagonal fold lines e Use Big Points or Use Small Points to change the size of the symbols that represent genes e Show Gene Names or Hide Gene Names to show or hide gene names that appear beside the genes Copyright 1998 2001 Silicon Genetics 3 16 Viewing Data in GeneSpring Tree View Tree View The Tree view allows you to visualize your experiment as a mock phylogenetic tree or dendro gram In a tree genes having similar expression patterns are clustered together 1 From the navigator open the Gene Trees or the Experiment Trees folder 2 Click a tree name to select it If there are no trees available for viewing you will need to create one Biel Es GeneSpring 4 1 Yeast Genes all genes File Edit View Experiments Colorbar Tools Window Help Gene Lists Experiments _ Gene Trees _ Experiment Trees _ Classifications _ Pathways _ Array Layouts __ Drawn Genes _ External Programs Bookmarks Scripts NAME OF TREE PARAMETERS OF
61. are all of the experiments selected in the white Correlations box If X is between the minimum and maximum correlations specified in the Clustering win dow then the gene in question passes the correlations To Delete an Experiment from the Current Clustering 1 Click the name of the experiment in the white Experiments to Use window highlighting it 2 Click the Remove button Similarity Definitions The equations used to determine the nine types of correlations are described in detail in Equa tions for Correlations and other Similarity Measures on page L 1 The default correlation is the Standard Correlation Standard correlation a b a b Minimum Distance and Separation Ratios To make a tree GeneSpring calculates the correlation for each gene with every other gene in the set Then it takes the highest correlation and pairs those two genes averaging their expression profiles GeneSpring then compares this new composite gene with all of the other unpaired genes This is repeated until all of the genes have been paired At this point the minimum distance and the separation ratio come in to play Both of these affect the branching behavior of the tree The minimum distance deals with how far down the tree discrete branches are depicted A value smaller than 001 has very little effect because most genes are not correlated more closely than that A higher number will tend to lump more genes into a group making the groups less sp
62. at the average location of each group of genes With each iteration genes are reassigned to the group with the closest centroid After all of the genes have been reassigned the location of the centroids is recalculated and the process is repeated until the maximum number of iterations has been reached gt GeneSpring 4 1 Rat Genes all genes loj x Fie Edt View Experiments Colorbar Tools Annotations Window Help set6 42 genes colored by test set 29 genes colored by test 5 04 Normalized 5 04Normalized seti 8 genes colored by test Figure 5 4 A k means Cluster display in a Split Window Copyright 1998 2001 Silicon Genetics 5 9 Clustering and Characterizing Data in GeneSpring k Means Clustering To Perform k means Clustering 1 Select Tools gt Clustering The Clustering window will appear as in Figure 5 5 inixxi EH Gene Lists Cenesto Use B eon Choose from Genes like YMR199 CLN1 0 95 Additional restrictions on genes for clustering s Remove 117 of 117 genes pass restrictions 4 Experiments to Use BS Experiments Add gt gt weight 1 0 Yeast cell cycle time series no 90 min Default Interpretati hisses Random 4 gt Clustering Method k means rK Means Remove Number of Clusters 5 Maximum Iterations 100 Measure similarity by Standard Correlation E Star From Current Classification V Animate Display While Clustering
63. axis to be continuous expression levels in between conditions will be interpolated Copyright 1998 2001 Silicon Genetics 3 31 Viewing Data in GeneSpring Changing the Coloring Scheme Trust The horizontal axis of the colorbar indicates the degree to which you can trust your data where dark or unsaturated colors represent low trust and bright saturated colors represent high trust You can assign trust values for each gene when you load your experiment or allow GeneSpring to create trust values automatically the latter numbers are listed in the Gene Inspector in the Con trol column To enter your own numbers see The Experiment Wizard on page D 1 The fol lowing are the guidelines by which GeneSpring automatically creates trust values In two color experiments the trust value is usually the control channel typically Cy5 unless you do a per chip normalization in which case it is the control channel x the median of the control channel x the median of the signal channel For Affymetrix and other one color experiments the trust value is constructed based on the normalizations you have chosen If you accept the default normalizations for Affymetrix data use distribution of all genes using the 50th percentile and normalize to the median for each gene then trust is the median value of the chip x the median value of the gene If you choose to use distribution of all genes using the 50th percentile and normalize to
64. cell Fill Sequence Down Allows you to fill down as described above but additionally will rec ognize a simple numeric or alphabetic sequence and continue it The Experiment Parameters Window To reach the Experiment Parameters window select Experiment gt Change Experi ments Parameters There are four special rows at the top of the Experiment Parameters window Parameter Name This box should be filled with a short description of the parameter It will be used in the main GeneSpring navigator it will be much easier to read later if you use short names or names with distinctive beginnings You can paste or type directly in this text box Parameter Units These are any units that will apply to the parameter values For example the parameter values of drug concentration could be 10 ppm 20 ppm 30 ppm and 40 ppm You can paste or type directly in this text box Numeric Selecting this cell will result in a yes no drop down menu Choose one or the other the indicate whether or not the parameter values are numeric If you click Yes GeneSpring will automatically order the parameter values in numeric order from smallest to largest Please refer to Re order the Parameters on page 2 10 before you make an permanent decisions Logarithmic Selecting this cell will result in a yes no drop down menu Choose one or the other the indicate whether or not these parameter values should be displayed on a logarithmic scale Copyright 1998 2001
65. conditions These conditions are listed in the far right side of the tree view If one of the parameters has been designated as a Copyright 1998 2001 Silicon Genetics 3 19 Viewing Data in GeneSpring Tree View continuous parameter it will be shown directly beneath the genome browser The continuous parameter can be viewed with the animate command if you first change the coloration to a single condition 1 Right click in the genome browser 2 SelectOptions gt Color by a Single Condition 3 Select the Animate checkbox or use the slider at the bottom of the screen to change the condition displayed Horizontal Genes Vertical Genes It is possible to change the orientation of your Gene or Experiment Tree 1 Right click in the genome browser and select Options gt Vertical Genes Copyright 1998 2001 Silicon Genetics 3 20 Viewing Data in GeneSpring Ordered List View Ordered List View Allows you to view a gene list in the order of its associated values Values are listed in descending order If you do not have associated values genes will be ordered according to the way they are listed in the Master Gene Table Vertical lines representing genes are proportional to the gene s associated number To view genes in an ordered list go to View gt Ordered List Your list will appear in its order S GeneSpring 4 1 Yeast Genes ACGCGT in all ORFs File Edit Vievy Experiments Colorbar EH Gene Lists _ Gene Ont
66. control channel to calculate ratio cutof f 10 e Per chip Distribution of all genes using 50th percentile cutoff 0 01 e Options Use background correction if necessary anything but absent 2 4 Copyright 1998 2001 Silicon Genetics Creating DataObjects in GeneSpring The Experiment Autoloader BioDiscovery Imagene 4 will automatically display all information flagged as Present or Unknown e Per spot Use control channel to calculate ratio cutoff 10 e Per chip Distribution of all genes using 50th percentile cutoff 0 01 e Options Use background correction if necessary anything but absent Incyte GEMTools 2 4 will automatically display all information flagged as Present or Unknown e Per spot Use control channel to calculate ratio cutoff 10 e Per chip Distribution of all genes using 50th percentile cutoff 0 01 e Options Use background correction if necessary anything but absent Internet Download will automatically display all information flagged as Present or Unknown e Per spot Use control channel to calculate ratio cutoff 10 e Per chip Distribution of all genes using 50th percentile cutoff 0 01 e Options Use background correction if necessary anything but absent Replicates If you have three or more experiments with the same samples GeneSpring will automatically nor malize to the median for each gene Please refer to Dealing with Repeated Measurements on page G 16 for a mathematical explanation of
67. correlation Find Similar Complex Correlations Save As Drawn Gene Cancel Help Figure 3 18 Gene Inspector window for gene MET3 Yeast Cell Cycle Gene Identification Section Information on the selected gene from the master gene table is displayed in the upper left corner of the Gene Inspector in the Gene Identification section Copyright 1998 2001 Silicon Genetics 3 38 Viewing Data in GeneSpring The Inspectors The Data Table The table in the upper right corner is the Data Table It contains the following information e Description The condition under which the measurement was taken e Normalized tThe normalized data value For information about normalizations See Exper iment Normalizations on page 2 21 e Control The control strength for the gene For information about control strengths See Per gene Normalizations on page 2 25 e Raw The raw value of the data just as it came off the chip or out of the scanner e t test p value tThe t test p value is applicable only to replicated data For information on this calculation see The T test P value on page 3 39 e Flags Flags indicate whether or not your data is reliable Whether or not you have flags will depend on your instrumentation and what you have entered into your master gene table See Measurement Flags on page J 12 The T test P value In cases where there is replicate data a one sample Student s t
68. data available and there should have been Marked with an A for absent or F for failed Flags assigned by GeneSpring e Unavailable Data if there is no flag in the column GeneSpring will assign that measure ment a U Only measurements at the highest available level of flag are combined and treated as replicates in GeneSpring version 4 0 The order of flag precedence is P M U A If one or more Ps are present only Ps are used if not and one or more Ms are present then only Ms are used etc Sum mary statistics are collected over these cases and stored with the corresponding flag All other flag data is discarded for the gene This is done when the experiment is loaded into GeneSpring and is not affected in any way by later user choices about which codes are to be used or displayed The only way to avoid this is to not declare a flag column during data load which means that the flags would not be available for other uses For information about measurement flags and how to load them into your experiment please refer to The Flags panel will appear If your experimental data contains a column indicating whether the experiment worked for each gene GeneSpring can incorporate this data Select the Yes cir cle on page D 11 and Measurement Flags on page J 12 Appendix G 17 Copyright 1998 2001 Silicon Genetics Normalizing Options Negative Control Strengths Negative Control Strengths Some types of micr
69. e are the parameter values associated with parameter 1 numbers If the answer is yes enter true after ParameterlIsNumber and if the answer is no enter false Parameter IsNumber enter either true or false ParameterlIsNumber true Parameter2IsNumber false Parameter3IsNumber true Parameter4IisNumber false Copyright 1998 2001 Silicon Genetics Appendix J 2 Installing from a Text File Define Your Parameters 7 This question is only applicable to those parameters defined by a number I e for those parameters for whom the answer to question 6 is true Would you like the number defining parameter 1 graphed on a logarithmic scale If this answer is yes enter true as the object value following Parameter IsLogarithmic If the answer is no either do not enter the ParameterlIsLogarithmic line or type false as the object value The answer to this question is automatically false if a number does not define the parameter in question Parameter IsLogarithmic false ParameterlIsLogarithmic Parameter2IsLogarithmic Parameter3IsLogarithmic Parameter4IsLogarithmic nter either true or false false false false 8 Of the following four choices choose the most appropriate display for parameter 1 You may alter your choice within GeneSpring the display you are indicating here will simply be the default display See Definitions of Parameters on page 2 11 for mo
70. file is name e coli In this example name is the object name and e coli is the object value The object value can be thought of as the answer to the question posed by the object name In the genomedef file the order of lines is not significant but the case lower or upper case of letters is significant The spelling especially of the object name is also significant Blank lines and lines beginning with the number character are ignored Appendix l 1 Copyright 1998 2001 Silicon Genetics Installing a Genome from a Text File The genomedef File Define Your Genome This section is designed to help you create a genomedef file for a particular genome and there fore it is written as a series of questions for you to answer There are two examples following each question The first is the generalized form of the answer including the generalized object name and what sort of response constitutes a correct object value The second bold faced example is an example of an actual answer to the question Some of the lines the questions represent are required and others are not each question will be annotated accordingly The genome e coli is used as the example throughout this section 1 Enter the name of your genome as you wish it to appear in GeneSpring This line is required name the name of the genome name e coli 2 Ifyou are using a Master Gene Table to define your genome enter the complete file name of the fil
71. for more details on the types of numbers Gene Spring attaches to gene lists e Gene List Note Any notes attached to a gene list This options appears only if a gene list note exists e Systematic Name 7he systematic name is not listed in the Copy Annotated Gene List win dow but is automatically saved in the first column of a gene list It appears when you paste or open the gene list in a new application Identifiers e Common Name A non systematic way of referring to a gene e Synonyms Other names entered for your gene list e GenBank A gene s GenBank Accession Number if known e EC A gene s EC Enzyme Commission number if known e PubMed A gene s PubMed identifier e DB id A reference used to identify a gene within GeNet Copyright 1998 2001 Silicon Genetics 6 4 Exporting GeneSpring Data Exporting Gene Lists out of GeneSpring Normalized Data Average The mean of any normalized replicates in the experiment Minimum The minimum normalized signal values for each gene Maximum The maximum normalized signal values for each gene Flags Any measurement flags associated with genes in the list Standard Error the standard error of the normalized values for each gene Standard Deviation The standard deviation the square root of the variance of the normal ized values for each gene t test p value tThe t test p value which measure the significance of differential gene expres sion in each condit
72. from the pop up menu A window will open 3 Open a second sample or condition from the Experiment menu in the mini navigator of this window Note that you have already selected the first condition to be compared 4 From the pull down menu choose whether you want the signal in the first sample or condition to be greater than less than or equal to that in the second sample 5 Enter a fold factor in the by at least a factor of field 6 Select a type of data from the pull down menu Data Types for Restrictions You can change the type of data on which to base the restriction by choosing from a drop down list in the applicable window Depending on which feature you are currently using you may have access to only some of the options in the following list e Normalized Data the values that GeneSpring displays in the Normalized column in the Gene Inspector e Raw Data unnormalized experimental data Note if your computer is set for a default lan guage that is not English please make sure a consistent convention for decimal markers is fol lowed e Control Signal the normalization denominator e Number of Replicates the number of samples in each condition Copyright 1998 2001 Silicon Genetics 4 7 Analyzing Data in GeneSpring Filter Genes Analysis Tools e Range of Normalized Data the difference between the minimum and maximum of the nor malized data You can use the Range of normalized data feature if you want genes with f
73. gen Normalizations 27 4 5 cscs ccd oem seats aun aie nies tas a a a a 2 25 Normalize to Median For Each Gene 0 ccccescceeccesseeeseeceeceeeeeeeeesseecsaeenteeesnes 2 25 Normalizing to Sample s siss ccsicisntean ethane ese eattyetiads 2 25 Miscellaneous oniinn aE iu aneaecataeiee mets Gene Reese 2 26 Global Error Models coca 58 5 Sicasteasuees taceavas as vad a a Ruane males eee dass 2 26 Using the Global Error Model lt hacsloxsdcvsacisia desatinncsaaeces steeds duesoaeeasiaeede 2 26 Technical Details aissccsts sqaetanseaxdsasearisriactiieed ada ade anneal sen ees 2 28 Chapter 3 Viewing Data in GeneSpring eesseeessoessoessocessecssoossoossoosssoessoessoossoossssssssee 3 1 Using Genome Browser ace dcnskis erica ae Sint Aes carats 3 1 Changing Genome Browser Element cccssccssccessceseccssecesscesssccssccnsecennees 3 2 Splitting Windows aranea ta a Maser aes EE E E E Aeron miwhe 3 3 Displaying a Gene Liste seine aana nat E EEA EES AANS AARTE EA AEE iaai 3 4 Finding and Selecting Genes ccccsisscisscssterpusedectsshteistitastecantede cane natin deequntiens 3 4 Finding Genes asean es Sa ca Falah tas tat TA a ota id 3 4 oS SC ITN GENES da e cess au se catactnes tus ne Sones N Seca a Shanes ae mane ses ee 3 5 Showing Hiding Window Display Elements cccccceescesseeesceeeeeeeeeeeeeeeseeesaeenes 3 6 Graph VG east ets ta sit sad dh hash eet ak iat Sa NB ste et clea N ais aia 3 7 Bar Graph VIEW ec moea id sea es seta E
74. going to react in a similar way under similar conditions often it is when the expression patterns are not similar that the results are interesting This is where graphs of parameter values defined as color coded conditions are useful as they allow you to easily com pare varying conditions of the same gene Copyright 1998 2001 Silicon Genetics 2 14 Creating DataObjects in GeneSpring Annotation Tools Annotation Tools The Annotations menu in GeneSpring allows you to update annotations make gene lists based on annotations and build gene ontology tables You can annotate almost any data object in Gene Spring by adding notes in the various inspectors Annotations can also be searched using the Find Gene feature in the Edit menu See Finding Genes on page 3 4 for details Updating your Master Gene Table with GeneSpider After you have loaded a new genome you can make sure it contains the latest information from the genome databases on the World Wide Web by using GeneSpider To use GeneSpider you will need to have GenBank accession numbers in your master gene table GenBank accession numbers are usually added to column 10 of the appropriate gene in the master gene table separated by semicolons For details on adding information to your master gene table see Your Master Gene Table file on page H 1 To Update Annotations using GeneSpider 1 Select Annotations gt GeneSpider Pre 4 1 users Select Tools gt GeneSpider Choos
75. have negative controls or do not want to normalize your sample using the data from them select the No circle Answering Yes to the first question Do you have any genes designated as negative controls initiates a second question If you are using negative controls you must have a file listing them one gene name per line This file should be in the same sub directory as your experi mental data Inthe Negative controls file name box enter the name of the file list ing your negative controls For a mathematical illustration of this normalizing option please refer to Normalize to Neg ative Controls on page G 2 a Select the Next button to continue The Normalizations Control Channel Values panel will appear You will only see this panel if you have already told GeneSpring your sample has control channel values for each gene If you have control channel values for each gene to indicate the trust you have in the experimen tal data for each gene you probably want to normalize the genes by dividing their control strength by the control channel s control strength If you have a background signal for either or both of these values it is subtracted from the signal intensities before they are divided For more information on this normalization option see Normalizing Options on page G 1 If you wish to use this normalization select the Yes circle If you do not wish your data to be normalized using the control channel values leave th
76. in your master gene table for each gene in the genome you want to translate from In the second column insert unique identifiers for the corresponding genes from the genome you want to translate to In the example below SGD locus numbers have been used to identify genes in the yeast genome first column and GenBank accession numbers to identify genes in the human genome second column Yeast Human CPR1 M80254 YDL193w U82319 PABI Z48501 KGD2 D26535 YKR095w M18533 YJLO95w U02687 YDL140c S69370 3 Save this file with the name of the genome you are translating to and the extension homology Using the above example this would be Human homology note that this is case sensitive Note that if you have a pre 4 1 version of GeneSpring you will need to take an additional step Open the genomedef file in the folder of the genome you would like to translate to and add the following AcceptedDirectTranslations Name of the genome you are translating to without the exten sion In the above example this would be AcceptedDirectTranslations Human Copyright 1998 2001 Silicon Genetics 4 31 Analyzing Data in GeneSpring Scripts 4 Restart GeneSpring 5 Right click a gene list in the genome you wish to translate from and select the Translate menu option A submenu containing the genome you have translated to will appear Select this option 6 Open the genome you have translated to Y
77. lines to be skipped b Select the Next button to continue 11 The Region Normalization panel will appear This panel allows you to employ region normal izations a Select Yes at the question Did each of your sample s use multiple arrays or sections of a single array that require separate normalization if a sample in your experiment was pre formed on more than one array or if there is some reason you want the sections on the arrays normalized individually You will need to enter the column of your experimental data file containing the region desig nation Make sure the spelling and capitalization you enter is exactly the same as is used in the data file Copy and paste if you can to make sure the spelling and capitalization is identical If the region is the only entry in the region designation column or if it is a suffix attached to the column s entry then you need to type all of the different region designators the different suffixes or column entries defining which gene was in which region in the List all possible region column entries or suffixes box The different region designators must be separated by spaces or else GeneSpring will read them all as one entry Appendix D 8 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard 12 13 If the region designators used in your experimental data file are neither unique column entries nor suffixes see Entering region specifications
78. list of options that appears from a sub menu or by right clicking Option click for Mac R Replicate Replicates can be multiple spots on the same array representing the same gene also referred to as a copy the same sample in more than one array or a biological replicate that is equivalent samples taken from more than one organism A parameter defined as a replicate is graphically a hidden variable no visual distinction is made based upon this parameter or its parameter values Regulatory Sequence the sequence upstream of a given gene to which regulatory enzymes bind determining the amount of expression of a particular gene S Sample the measurements taken from one or more chips containing a single liquid sample OR the data generated from a biological object placed onto an array or set of arrays Slider a horizontal scrollbar at the bottom of the GeneSpring window that changes the display of genes from one sub experiment to another e g in a time series experiment the slider moves the displayed genes across the different time periods T t test T tests calculate p values which measure the significance of differential gene expression in each condition Trust a measure of reliability of the data Two color experiment an experiment where a control is used V Variable a factor such as a disease drug concentration patient name pipette number time the strain of organism tested or who performed the experiment e
79. name of a 16x16 pixel gif file that includes an icon to be dis played in the navigator For example Icon sorter gif Command required the command line string required to run the program For exam ple Command Sort or Command perl sort pl Input required one or more numbers separated by commas corresponding to the type s of input that the external program requires see table XXX For example Input 2 5 Output required one or more numbers corresponding to the type of output that the external program sends to GeneSpring see table XXX Include existing table at the end of this section For example Output 2 UserParameters optional one or more user defined parameters separated by commas that are passed to the external program For example UserParameters Iterations 10000 UserParameterFill optional a text string to fill in blank values for the UserParameters above For example UserParameterFill none GeneListNumberDescription optional if the external program returns an ordered gene list back to GeneSpring For example GeneListNumberDescription Terminate With255 true if you want GeneSpring to terminate the external program input with ASCII 255 For example TerminateWith255 true InterModeDelimiter optional an ASCII code representing the character used to delimit multiple objects that are sent to the external program For example InterModeDelimiter 255 Copyright 1998 2001 Silicon Ge
80. navigator Each folder contains a specific type of information The labeled diagram and list below briefly explains the purpose of each folder Copyright 1998 2001 Silicon Genetics 1 13 Introduction GeneSpring Basics GeneSpring 4 1 Rat Genes all genes Fie Edit Yiew Experiments Colorbar Tools Annotations Window Help EH_ Gene Lists A PREUS E Fy Experiments B j Default Interpretation o All Samples Replicate Interpretation Le stage Embryonic f on stage Postnatal e stage Adult EH Gene Trees C Lich rat tree ch subtree Ey Experiment Trees D Lf test experiment tree EH classifications E i 32 cluster K Means for test Default Interpretation TE 43 SOM for test Replicate Interpretation EHL Pathways F E Array Layouts G H Drawn Genes H E External Programs FH Bookmans J Scripts K 23 0 nnt DO xX Om a a a Postnatal Adult Embryonic Magnification 1 Zoor Figure 1 4 The GeneSpring Navigator A During analysis you will create and work with interesting collections of genes known as gene lists These gene lists are stored in the Gene Lists folder By default GeneSpring makes and displays an all genes list containing all genes in the genome B The Experiments folder contains experiment information Experiments are divided into interpretations Experiment Interpretations tell GeneSpring how to treat and display your experiment variables called experiment parameter
81. normalization is not a separate mathematical formula the way the previous normalizations discussed in this chapter are Using this normalization means if you normalize to negative controls to positive controls or normalize each sample to itself you do not actually nor malize over each sample but rather perform the normalization over each region Hence the for mulas for these three normalization options become Normalizing to Negative Controls for a Region the control strength of gene A in region Y of sample X the median signal of the negative controls in region Y of sample X Normalizing to Positive Controls for a Region the control strength of gene A in region Y of sample X the median signal of the positive controls in region Y of sample X Normalizing Each Region to Itself the control strength of gene A in region Y of sample X the median of all of the measurements taken in region Y of sample X g p See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring and for how to define a region Appendix G 15 Copyright 1998 2001 Silicon Genetics Normalizing Options Dealing with Repeated Measurements Dealing with Repeated Measurements Single Data File Occasionally the raw experimental data in the data file for your sample will have more than one line devoted to a particular gene This may be because you did the sample twice or because you did the sample once but took
82. not count towards the sequence length specified hence ACGnnnCGT would be returned as an oligonu cleotide of length 6 Select whether the sequence is relative to the sequence upstream of other genes or relative to the whole genomic sequence The first option is far more common e The Probability Cutoff textbox indicates the level of significance P value needed for an oligomer to be listed in the results You may change this value if you wish Select the Search button The button will change toa Stop Search button The progress bar will lengthen as your search progresses Copyright 1998 2001 Silicon Genetics 4 27 Analyzing Data in GeneSpring Regulatory Sequences Viewing Regulatory Sequence Search Results The search results will be shown on the right hand Results area of the Find Potential Regulatory Sequences window Selecting the View Details button provides expanded results data that can be viewed by scrolling Selecting the View Genes for Selected Row button brings up the Conjectured Regulatory Sequence window Double clicking any of the sequences in the table on the left brings up the Conjectured Regulatory Sequence window Sequence The nucleotide sequence of the oligomer Observed The number of genes in the list where the oligomer was found P value The probability P Value that the number of occurrences in the list came about by chance Only nucleotide motifs with P values below the specified probability cutoff in this case
83. not cutting and pasting data you will need to create a folder called Experiments and place your experimental data files in that folder so they will be easy to find when you need them later in this process Files You will Need to Use the Experiment Wizard An experimental data file is the main file needed for loading an experiment Gene names need to be listed in the first column one name per line with the experimental data reported in subsequent columns Viewed in a spreadsheet it might look like this Gene Name Control Control Background Background Experiment Region Strength in Channel Signal Signal for Flag Experiment Strength the Refer 1 ence CLN1 510 110 10 10 P MEP2 9 19 9 9 M C If created in a spreadsheet program the file should be saved as a tab delineated text file If your computer is set for a non English language that typically uses commas for decimal mark ers GeneSpring will recognize this If for example your computer is set for French the comma will be recognized as a decimal marker You cannot use commas and periods interchangeably GeneSpring can also read experimental data from databases via an ODBC link Please refer to Installing from a Database on page E 1 e Pictures of the conditions during the experiment Pictures of a condition can be useful reminders of what was happening in an experiment at a given point in time In GeneSpring you can associate a ma
84. of the raw measured value The sample to sample variability includes the effect of both types of variation and the statistical separation of these effects is called variance components analysis The GeneSpring Global Error Model performs this variance components analysis and uses the estimates of these two compo nents of variation to accurately estimate standard errors and compare mean expression levels between experimental conditions When you turn the Global Error Model on the Error Model is used as the basis for e standard deviation representing the variability of individual population members e standard error representing the precision of the mean of the gene expression measurements in the condition with respect to the true condition mean e error bars corresponding to standard deviation or standard error in the Graph view and Gene Inspector e t test p value representing the statistical test of differential expression for a specific condition e color by significance coloring according to the t value from the t test of differential expres sion e tests between condition means using the Statistical Group Comparisons filter if the error model option is chosen To turn on the Global Error Model 1 SelectExperiments gt Error Models The Error Models window will appear 2 Ifyou have replicates for each condition check the Replicates box and select parameters to treat as replicates Click OK If not check the Deviation from 1
85. option of showing or hiding many of the elements in the GeneSpring window To change the visibility of these elements select View gt Visible and choose one of the follow ing options Picture Shows or hides the optional picture at the bottom right corner of the window Animation Controls Shows or hides the slider and the Animate check box at the bottom of the window hiding this check box does not disable the Animation feature Magnification Shows or hides the Magnification feature and the Zoom Out button at the bottom of the window hiding the Zoom Out button does not disable the Zoom Out menu option Secondary Picture Shows or hides your secondary picture when you are viewing two gene lists or experiments simultaneously in the genome browser Secondary Animation Controls Shows or hides the secondary Animation Controls check box and slider when you are viewing two gene lists or experiments simultaneously Navigator Shows or hides the navigator panel Hide All Hides everything in the window except the genome browser Show All Shows all elements Hide All in All Windows Hides everything in all windows except the genome browser Show All in All Windows Shows all elements in all windows Copyright 1998 2001 Silicon Genetics 3 6 Viewing Data in GeneSpring Graph View Graph View The Graph view allows you to visualize one experiment or a set of experiments by plotting the relative expression of each gene against exper
86. page G 1 NormalizeToExperiment true or false NormalizeToExperiment 0 Appendix J 18 Copyright 1998 2001 Silicon Genetics Installing from a Text File Colorbar Specifications Colorbar Specifications 37 The intensity of the colorbar in GeneSpring indicates how reliable the data for each gene is Indicate a raw control strength value to be considered very reliable a high control strength value an average a medium control strength value and an unreliable a low control strength value Any gene with a control strength control above the value indicated as a high control strength will be colored using the brightest color appropriate any gene with a control strength below the value given for unreliable data will be almost black in color The medium signal value gives the value for the mid point of the color bar and genes with a medium con trol strength are colored halfway between the two color extremes The default values are spec ified in the example If you do not indicate a high medium and low values specifically then the values GeneSpring will automatically use to determine the color bar are SignalHigh a high number this indicates high confidence in the data SignalMedium a medium number this indicates average confidence in the data SignalLow a low number this indicates low con fidence in the data SignalHigh 500 SignalMedium 150 SignalLow 50 These numbers are arbitrary They are intended
87. parameters interpretations is correct for parameter 1 either enter false as the object value or do not include the line beginning with ParameterlIsRepeat Parameter l Parameterl Parameter2 Parameter3 Parameter4 sRepeat IsRepeat IsRepeat IsRepeat IsRepeat either true or false false false true false e You wish to use parameter to separate the data into discrete graphs viewed next to each other on the same screen This is a non continuous parameter Follow Parameter1IsSet with the object value true if this is how you wish this parameter to be displayed If one of the other possibilities seems more correct for parameter 1 either enter false as the object value or do not include the line beginning with Parameter1IsSet Parameter l Parameterl Parameter2 Parameter3 Parameter4 sSet Isset Isse Isset IsSet ct ct ct either true or false false false false true 9 Enter the number or label applicable to each sample as it is associated with parameter 1 This is where you tell GeneSpring what each condition means as far as each parameter is con cerned Parameter Experiment either a value or a name associated with both the parameter indicated and the sample indicated For each parameter you must indicate a label to associate with every condition ParameterlExperimentl 0 ParameterlExperiment2 10 ParameterlExperiment3
88. parenthesis and the asterisk This tells Gene Spring to expect non numeric parametric values and then treat the data appropriately e The default setting for interpretation of parameters is as a continuous element please see Continuous Element on page 2 13 for details To have the parameters treated differently enter the following codes just after the parentheses e S means the data will be interpreted as a non continuous element also known as a dis crete element Please see Non Continuous Element Set on page 2 13 for details e C data will be colored by the different parametric values assigned automatically by GeneSpring In Figure E 2 each column would get a different color as time values 0 160 Please see Color Code on page 2 13 for details e R data will be interpreted as a replicate not shown Please see Replicate or Hidden Element on page 2 13 for details e Of course you can just enter all parameters with the default no code after the parenthe ses and change the interpretation later from within GeneSpring please see Changing the Experiment Interpretation on page 2 17 e For example for the parameter tissue type a non continuous non numeric parame ter the first column might look like tissue type S If you have no parameters give it arbitrary but meaningful names so you will be able to distin guish each sample from those in other columns 3 Data e There can only be one gen
89. save time by allowing a long series of data analysis steps to be performed at once Scripts are re usable and can be applied to any data set You can create your own scripts using Silicon Genetics Script Editor All scripts including complimentary scripts shipped with GeneSpring 4 1 are stored in the Scripts Folder Copyright 1998 2001 Silicon Genetics 4 32 Analyzing Data in GeneSpring Scripts Scripts in GeneSpring There are seven pre prepared scripts in the Script folder that you can use e Make Gene List from Text Search This script will find the genes annotated with either search term 1 or search term 2 and exclude all genes with search term 3 e Find Similar genes This script will make a gene list of similar genes for every gene on the input list if there are at least 5 genes with similar expression profiles in the input experiment e 2 fold expression change This script will make a gene list of all genes that are 2 fold over expressed or 2 fold under expressed in at least 1 condition in the input experiment e Clustering 2 fold change list This script will make a gene tree an experiment tree a k means classification amp a self organizing map using a list of all the genes that are 2 fold over expressed or 2 fold under expressed in at least 1 condition in the input experiment e Send Clustering Results to GeNet This script will make a gene tree an experiment tree a k means classification amp a self organizing map using
90. separate file name for each sample Experiment FileName complete name of the file containing the data from the sample indicated Experiment1FileName 1A0 txt Experiment2FileName 1A10 txt Experiment3FileName 1A20 txt Experiment4FileName 1A30 txt Experiment5FileName 1A40 txt Experimetn6FileName 1B0 txt Experiment7FileName 1B10 txt Experiment8FileName 1B20 txt Experiment9FileName 1B30 txt Experiment10FileName 1B40 txt Experiment11FileName 1AndromedaA0O txt Experiment12FileName 1AndromedaA10 txt Experiment13FileName 1AndromedaA20 txt Experiment14FileName 1AndromedaA30 txt Experiment15FileName 1AndromedaA40 txt Experimetn16FileName 1AndromedaBO txt Experiment17FileName 1AndromedaB10 txt Experiment18FileName 1AndromedaB20 txt Experiment19FileName 1AndromedaB30 txt Experiment20FileName 1AndromedaB40 txt Experiment21FileName 2A0 txt Experiment22FileName 2A10 txt Experiment23FileName 2A20 txt Appendix J 6 Copyright 1998 2001 Silicon Genetics Installing from a Text File Data File Header Lines Experiment24FileName 2A30 txt Experiment25FileName 2A40 txt Experimetn26FileName 2B0 txt Experiment27FileName 2B10 txt Experiment28FileName 2B20 txt Experiment29FileName 2B30 txt Experiment30FileName 2B40 txt Experiment31FileName 2AndromedaA0 txt Experiment32FileName 2AndromedaA10 txt Experiment33FileName 2AndromedaA20 txt Experiment34FileName 2AndromedaA30 txt Experiment35FileName
91. sequence is that common due to chance is 1 694e 7 However since 16 384 tests were done the false positive probability is really 0 277 Offset A C G I suggestion 10 35 22 5 20 22 5 9 Paka uek a Fuko APAT 8 ates iii AG 20 7 35 25 7 5 agi In 6 35 12 5 25 27 5 5 45 Pakal Wika 15 A 4 45 225062 n A ORF Distance Sequence YNL225C 492 TTGTTGGTCAAAACGCTCCAGAGATT YDR297V 432 GTGTGACTTGAAACGCGTTTTATCCT YOR144C 429 TT GCGATTGAAAACGCTACAAGAACA YKL199C 411 ATCTATAATGAAACGCCCGAGAAATT YDLOO3V 374 AATGTTCTTCAAACGCGTTTATTATA YDR507C 312 TCATCGAAGGAAACGCGTCAAATCCA YDL197C 302 CACCAATTCTAAACGCACAGTTGCAC OK Cancel Help Figure 4 6 The Conjectured Regulatory Sequence window Copyright 1998 2001 Silicon Genetics 4 29 Analyzing Data in GeneSpring Regulatory Sequences Two drop down menus File and List are located at the top of the window File Contains two commands Print and Close e Print Prints the list in the lower half of the Conjectured Regulatory Sequence window e Close Closes the Conjectured Regulatory Sequence window List Contains three commands Remove Item Make Gene List and Extend Pro moter e Remove Item Removes the highlighted item and its associated sequence motif from the list matching the common sequence motif being examined e Make Gene List Brings up the new Gene List window for you to name and save a new gene list When a gene list is produced based on the occurrence of a s
92. simultaneously Simply select a gene or gene list in one window and the same gene or gene list will automatically be selected in the other window To create a linked window go to the File menu and select New Linked Window Split Windows Another interesting way to view classifications is with the Split windows function The Split win dows feature will allow you to see multiple sets simultaneously in the main GeneSpring screen To reach the split windows command right click over any item in the classification folder or any folder of classifications and move the cursor down to Split window A small pop menu will appear Select one of the options If you selected Vertically the main screen of the genome browser will re arrange into several small screens Notice the number of genes in the upper right corner of each small screen While viewing split screens you can make changes in the experiment interpretation zoom and pan around the same way you do with unsplit screens 1 Right click over folder gt Use as Classification 2 Right click gt Split window gt Vertical 3 View gt Graph You can double click the banner bar to increase the screen size To unsplit the screen select View gt Unsplit window or right click over the original data object and select Split gt Neither You can also hide the labels appearing in the main GeneSpring screen All of the Hide and Show commands are simple toggle switches Re select that opt
93. test is calculated to test whether the mean normalized expression level for the gene is statistically different from 1 0 The t statistic is calculated as x 1 Sy i j _ N where X ped is the sample average of the normalized expression levels ial Kyr and Sy J gt oo z 14 Figure 3 19 The formula for t test i is the sample standard deviation of the replicates The value of t is compared with a table of the distribution of Student s t distribution with n 1 degrees of freedom to yield the significance level or p value for a two sided test that the mean gene intensity differs significantly from 1 0 The Browser Display The Gene Inspector browser shows the gene s expression over the experimental parameter time minutes in Figure 3 18 The browser image reflects the experiment interpretation in the main browser window The only view option available in the Gene Inspector is the Graph view Copyright 1998 2001 Silicon Genetics 3 39 Viewing Data in GeneSpring The Inspectors By right clicking on the browser you can use error bars in the browser display create a resizable picture of the browser or save a bookmark By right clicking and selecting Options you can change the vertical axis range show or hide many of the browser elements and switch your view from normalized to raw data For more information about the latter options see Using Genome Browser on page 3 1 For information about error bars see
94. that it only consid ers positive changes All negative values for the arc tangent transform of the ratio are set to zero This emphasizes only periods when new RNA is being synthesized This is how to compute an Upregulated correlation Make a new vector A from a by looking at the change between each pair of elements of a Do this for each pair of elements that would be connected by a line in the graph screen The value created between two values a and a is max atan a 1 a 7 4 0 Do the same to make a vector B from b Upregulated correlation A B A B Appendix L 5 Copyright 1998 2001 Silicon Genetics Equations for Correlations and other Similarity Measures Special Case Correlations Appendix L 6 Copyright 1998 2001 Silicon Genetics Creating an Array in GeneSpring Appendix M_ Creating an Array in GeneSpring In order to create an array layout file in GeneSpring you need at least one file to tell GeneSpring general information about the array size shape features format name etc This file should end in the extension ayout You usually need another file describing exactly which gene goes where The format of the ayout file is a series of lines order does not matter Each line consists of a property a colon and a value For example property value Blank lines and lines start ing with a number sign are ignored by GeneSpring The following properties are allowed in the file As always GeneSpring is case s
95. the GeneSpider is bringing back You may want to let this program run over your lunch hour or for very large genomes overnight Sequence Data GeneSpring loads in sequence data from a GenBank or EMBL file automatically If you have sequence data that is not in a GenBank EMBL file then the sequence data should be put into a separate file and formatted using the seg format A severely abridged example of the yeast seq file might look like the following gt CHR1 Chromosome I data CCACACCACACCCACACACCCACACACCACCACCACACCACACCCACACACACA GTGGGTGTGGTGTGGTGTGTGGGTGTGGTGTGGGTGTGGTGTGTGTGGG gt CHR2 Complete DNA sequence of yeast chromosome AAATAGCCCTCATGTACGTCTCCTCCAAGCCCTGTTGTCTCTTACCCGGA Appendix H 5 Copyright 1998 2001 Silicon Genetics Creating Folders for New Genomes Raw Data AGAATAGGGTACTGTTAGGATTGTGTTAGGGTGTGGGTGTGGTGTGTGTGGG IGTGGTGTGTGGGTGTGT gt CHR3 LOCUS SCCHR 315341 bp DNA PLN 25 NOV 1996 CCCACACACCACACCCACACCACACCCACACACCACACACACCACACCCA AGTGTGTGGGTGTGGGTGTGTGGGTGTGGTGTGTGGGTGTGGTGTGTGTGTGGTGI GTGGGTGTGGGTGTGTGGGTGTGGTGGGTGTGGTGTGTGTG If you have multiple chromosomes they should be named sequentially CHR1 CHR2 and so on If there is only one chromosome name it CHR1 The seq format is not the same thing as the FASTA format There is an example of the FASTA format at http www ncbi nlm nih gov BLAST fasta html Where Do Put My Data Files The files shou
96. the Spearman confidence discussed in Spearman Confidence on page 3 except it is based on the two sided test of whether the Spearman correlation is either significantly greater than zero or significantly lower than zero There is a high Two sided Spearman confidence value if the absolute value of the Spearman correlation is large and has a small p value meaning there is a low probability to find a correlation with absolute value this large This similarity measure is really good for answering the question What genes behave similarly to a specific gene and at the same time what genes behave opposite to a specific gene It should probably not be used for the advanced clustering algorithms such as k means and hierar chical clustering because the genes with high two sided confidence values are really a mixture of similar and dissimilar genes Copyright 1998 2001 Silicon Genetics Appendix L 3 Equations for Correlations and other Similarity Measures Special Case Correlations This is how to compute a Two sided Spearman confidence If r is the value of the Spearman correlation as described above then Two sided Spearman confidence probability you would get a Spearman correlation of r or higher or r or lower by chance Distance Distance is not a correlation at all but a measurement of dissimilarity Distance is based on the measurement of Euclidian distance between the expression profile for gene A defined by its
97. the experimental data file pertaining to each sample The Describe your Data Files panels are large Please double click the banner bar to expand the panel to fill your screen so you will not miss any of the possibilities a To begin describing your files to GeneSpring you must select one of the options in the drop down menu at the top of this panel You have three selectable options to describe the files containing your data e All my samples are in one file First and easiest if all of your samples are in one data file selectAl1 my sam ples are in one file In the table at the bottom of the panel fill in the field labeled File Name with the name of the text file containing your sample s data When your data is all in one file the formats will all be the same Be aware as soon as you leave this panel by clicking the Next button the changes will be irrevoca ble You may see the quick flutter of an error message reminding you of this My samples are in multiple files that share a common format If your samples are in different files with exactly the same format select the default setting My samples are in multiple files that share a com mon format Enter the name of the file containing the sample data for each experiment in the table Each file should be entered in the white boxes of the column labeled File Name in the same row as its sample If your data files are where GeneSpring expects them to be i e in the correct d
98. the first Set button 3 Click your test set the set where the parameter value of interest is unknown and click the second Set button 4 Open the Gene Lists folder in the mini navigator and click a gene list to be used in the selec tion process Click the third Set button 5 Specify a parameter type inthe Parameter to predict box 6 Choose a Maximum Number of Genes to be used in the prediction 7 SpecifyaNumber of Neighbors Generally this number should be no more than half the size of a single class and no less than 10 Copyright 1998 2001 Silicon Genetics 5 15 Clustering and Characterizing Data in GeneSpring The Class Predictor 10 Specify a P value Cutoff The P value cutoff is a threshold such that if there is not suffi cient evidence in favor of a particular class no prediction will be made The P value cutoff is a ratio of the probability that the prediction was made by chance for the two classes If you have more than two classes the ratio is the lowest P value divided by the next lowest P value Click Predict Test Set to make a prediction or Crossvalidate Training Set to evaluate how well the prediction rule can be used to predict the parameter values of the training set Selecting Save Minimal Experiment saves an experiment containing all of the sam ples in your training set but including only the predictor genes This is useful if you are mak ing multiple predictions using the same training set and don t w
99. the measurements twice If the same gene name is reported multiple times on different horizontal lines in your data file GeneSpring will automatically consider the measurements repeats and average all of the control strengths together GeneSpring will report the average to you and it will keep track of the minimum and maximum values for each gene but GeneSpring will not be able to access the particular values falling between the minimum and maximum values The formula for averaging a repeated gene is the signal strength of gene A1 the signal strength of gene A2 the signal strength of gene An N This process is done for every gene repeated in a data file and it is done before any other normal izations are applied to the raw values Frequently samples are repeated with exactly the same parameters but are reported in different data files If this is the case the fact the samples are repeats is represented via parameter The same normalization is employed when dealing with an experimental parameter considered to be a repeat but in that case the averaging takes place after the raw data for each gene has been normal ized See Change Experiment Parameters on page 2 8 for more information about repeats reported in separate data files Mathematical Illustration of the Dealing with Repeated Measurements in a Single Data File Method Given this raw data with four repeats of YMRI99W marked with the arrows gt Y MR199W 1
100. to be general benchmarks not hard boundaries Graph Specifications The values indicated here can be altered within GeneSpring you are simply setting the default values here 38 To allow you to inspect the genes expression profiles closely GeneSpring does not graph the entire y axis the expression level axis but only the portion most genes profiles fall into Indicate the range of expression levels GeneSpring should graph LowerBound Indicate the lowest expression level to graph on the y axis UpperBound Indicate the highest expression level to graph on the y axis LowerBound 0 UpperBound 5 0 A lower bound of 0 and an upper bound of 5 are the default settings of GeneSpring Appendix J 19 Copyright 1998 2001 Silicon Genetics Installing from a Text File Graph Specifications Appendix J 20 Copyright 1998 2001 Silicon Genetics Experiment File Formats Raw Data Appendix K Experiment File Formats You can install a new experiment in one of several ways by using the Experiment Installation Wizard see The Experiment Wizard on page D 1 or by creating a experiment file by hand see Installing from a Text File on page J 1 Both experiment entry methods may involve a number of corollary files Only one file type is necessary for installing an experiment e Experimental data file s containing the genes names and raw data for each sample in the experiment Please refer to Raw Data
101. to normalize by using the Affymetrix software You will need to do this because the GeneSpring analysis algorithms assume your data is normalized to a median of 1 GeneSpring will use the following formula the signal strength of gene A in sample X hard number in sample X You can use this normalization in concert with Normalize Each Gene to Itself Please refer to section The Normalizations Each Sample to a Hard Number panel will appear In this panel you tell GeneSpring if you want to normalize your samples to a value you enter You would normally only use this function if you have pre normalized data such as data prepared with Affymetrix s Global Scaling In that instance you would want to divide all data by 2500 or what ever number you chose to normalize by in the Affymetrix software You will need to do this because the GeneSpring analysis algorithms assume your data is normalized to a median of 1 on page 14 or to the Use Constant Values on page 2 24 for more details Appendix G 7 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalizing Each Gene to Itself Normalizing Each Gene to Itself This normalization method is intended to remove the differing intensity scales from multiple experimental readings It normalizes each gene to itself so the median of all of the measurements taken for that gene is one With this normalization you may graph a set of similar genes defined as similar by using the cor
102. when they are not specified in their own col umn or as suffixes within another column on page K 5 for how to import this information into GeneSpring You will not be able to enter this experiment using the Wizard For a mathematical illustration of this normalizing option please refer to Normalizing Options on page G 1 b Select the Next button to continue The Gene Name panel will appear This panel tells GeneSpring which column of your experi mental data file contains the gene names and whether the gene name is the only entry in its column a Enter the name or number of the column containing the gene name in the box labeled Enter the gene column name If you are entering the column number count the columns from left to right starting from one Make sure the spelling and capitalization is perfectly consistent with your file when you are entering the column names b Select Yes at the second question Does this column contain only the desired gene name without suffixes or prefixes only if the gene name reported in the experimental data is exactly like the gene name listed in the table of genes file defining the genome c Select Yes in the second question if there are prefixes suffixes or region designators which are frequently noted as prefixes or suffixes in the gene column If you do this the next two panels presented to you will be the Gene Name Prefix Removal panel and the Gene Name Suffix Removal panel If fewer th
103. window Do this by placing your cursor in the box highlighting the existing value and then typing in your preferred value 2 Click the Find Similar button The New Gene List window will appear which includes the genes in that list as well as lists that are similar to your new gene list In views where lists can be ordered such as the Ordered List view and Compare Genes to Genes view lists made with the Find Similar command are ordered according to correlation coefficient in descending order Copyright 1998 2001 Silicon Genetics 4 13 Analyzing Data in GeneSpring Making Lists with the Complex Correlation Com mand Making Lists with the Complex Correlation Command The Complex Correlation command in the Gene Inspector allows you to set up complex correla tions against the inspected gene These correlations may involve more than one experiment or condition or extra restrictions on experiments To Make Lists with the Complex Correlation Command 1 Access the Gene Inspector by double clicking on a gene this may be easier after zooming in Or a SelectEdit gt Find Gene b Enter in the name of your gene c Press Ctrl I Click the Complex Correlations button in the bottom left corner of the Gene Inspector window This will open the Multi Experiment Correlation window Choose a gene list from the Gene List folder in the navigator by right clicking the list and selecting Set Gene List To add an experiment or conditi
104. you can filter them out by setting a minimum expression value to be met in at least one condition gt Expression Level Percentage Restrictions i 0 x Experiment Yeast cell cycle time series no 90 min This will find genes with values in a given range for some proportion of the conditions in this experiment Expression Level Constraints Minimum Maximum In atleast 3 out of a total of 16 conditions Restriction applies to Normalized Data OK Cancel Figure 4 2 The Expression Level Percentage Restriction window To perform an Expression Level Percentage Restriction complete the following fields e Minimum the smallest value any gene can have and GeneSpring will still allow it in your list also known as the cut off value e Maximum the largest value any gene can have and GeneSpring will still allow it in your list e In at least out of a total the number of conditions in the total experiment where genes must meet the specified requirements This line can refer to the whole experiment Adding any number where will cause GeneSpring to search every sample to determine if the gene passes e Restriction applies to the type of data on which your restriction will be based Please refer to Data Types for Restrictions on page 4 7 Restricting by Statistical Group Comparison The Statistical Group Comparison restriction finds genes with statistically significant differences in expre
105. you use the pasting option you may need to create the positive and or negative control files asso ciated with the layout file The layout file tells GeneSpring where to find other files associated with the experiment If you load in experiments using a html file then you will need to create a layout file if each sample in Copyright 1998 2001 Silicon Genetics Appendix K 2 Experiment File Formats What format does this data need to be in your experiment involved more than one array and or if the experiment used positive or negative controls Frequently the same layout file can be used for more than one experiment There are four possible lines in a layout file Each line is either blank or a line of the form object name space colon space object value Object name object value An example of this is IncludePosControls false Here IncludePosControls is the object name and false is the object value The object value can be thought of as the answer to the question posed by the object name In the layout file the order of lines is not significant but the case lower or upper case of letters is significant The spelling especially of the object name is also significant Usually when an experiment looks like it is not installed correctly it is because of a spelling or capitalization error Using the copy Ctrl C and paste Ctrl V functions will help prevent this type of error This section is designed to help you create a l
106. your Database to Gene S Prine 2a suid ho disk a eecid adeeb aden eieaaeons E 4 Entering your Prepared Database into GeneSpring 0 eeceeeceteeneeereeeeeeeeceeeenneeaee E 5 Entering more Complicated Data from a Database 00 0 eceeccesceseeseeereeeeeeeeenseeneeeaee E 6 Appendix F Copying and Pasting Experiment sscccssscccssccsssssccessescesssscessees F 1 Prep ration for Pasting sseni userei isr ee a a a E A Aaaa iaai ana biaa F 1 Most Common Mistakes in Pasting s sssseseseessesessssessessrssressessrssressessresresseeseese F 3 Pasting your Experiment into GeneSpring ssessssssessesessseessessrssresseesesresseesees F 4 Copying an Experiment or a List Out of GeneSpring s sssssssssessessssssesseesrssrssseeseese F 4 Appendix G Normalizing Options ssessseessooesoossssessseessocssoossoosssosssoesssoessoossoosssssessee G 1 Background Subtractions sssesssesesseesseeseesessseesesresseessestrsstessesersseossessresresseeseseessee G 2 Normalize to Negative Controls sssssssssessesessssesseserssressessrssrosseestssesseesesersseesseseese G 2 Mathematical Illustration of the Normalize to Negative Controls Method G 2 Normalize to Control Channel Values for Each Gene 0 ccceccceesseeeseeeseeeteeeeseeees G 3 Mathematical Illustration of the Normalize to a Control Channel Value for Each Gene Method cceccceccccesceescesteceseeeseeeneeesseenes G 4 5 Copyright 2000 2001 Silicon Genetics Normal
107. your sample using positive controls leave the No circle selected a To indicate you have positive controls for normalization select the Yes circle This nor malization method takes the average signal intensities of all of the positive controls and divides each gene s signal intensity by that number For more information about this normal ization option see Normalizing Options on page G 1 If you are using positive controls you must have a file specifying what the positive controls are called listing the gene names one per line This file should be in the same sub directory as your experimental data In the Positive controls file name box enter the com plete name of the file listing your positive controls Sometimes something will go wrong with the positive controls and you will get very low val ues for all of them which you will not want to use for normalization purposes In the Enter lower cut off for positive controls box indicate the minimum average the positive controls must have such that dividing each genes control strength by the average of the positive controls will not artificially inflate the noise of the genes The default setting for the cut off value is 10 For a mathematical illustration of this normalizing option please refer to Normalizing Options on page G 1 b Select the Next button to continue 24 The Normalizations Each Sample to Itself panel will appear In this panel you tell Gene Spring i
108. 0 The number indicated in the example 10 is the default cut off value If you do not enter this line this is the cutoff value GeneSpring will use Normalizations Each Sample to Itself 32 Do you want to normalize your data by making the median of all of your measurements 1 for each sample in your experiment If you have not already preformed normalizations on your data you generally want to use this normalization option For more information about this normalization option see Normalizing Options on page G 1 NormalizeNoControl either true or false NormalizeNoControl true 33 If you are not normalizing each sample to itself skip this question and the associated experi ment file entry Sometimes something will go wrong with the experiment and you will get very low values for everything Indicate the cut off value by telling GeneSpring not to raise all of the control strength values up to a median of 1 if their average is below this number NormalizeMinRange Specify the cut off value telling GeneSpring not to raise all of the control strength values up to a median of 1 if the average control strength is below this number NormalizeMinRange 10 The number indicated in the example 10 is the default cut off value If you do not enter this line this is the cutoff value GeneSpring will use Appendix J 17 Copyright 1998 2001 Silicon Genetics Installing from a Text File Normalizations Each Gene to Itself Nor
109. 0 Copyright 1998 2001 Silicon Genetics Installing from a Text File The Control Channel Value The Control Channel Value These questions only apply if your sample has a control channel which is generally only applica ble to two color experiments such as Incyte or Sentini experiments If your data does not have control channel values skip this section and the associated experiment file entries 19 If your data has control channel values which column of your data file gives the reference value If your data does not have control channel values skip this question and the associated experiment file entry Experiment ReferenceColumn number of the column containing the control channel values for the experiment indicated Experiment1lReferenceColumn 6 Experiment2ReferenceColumn 11 Experiment3ReferenceColumn 16 Experiment 4ReferenceColumn 21 Experiment5ReferenceColumn 26 Experiment6ReferenceColumn 31 Experiment 7ReferenceColumn 36 If your data is all in the same file you will have to indicate the reference column for each sample illustrated above This is also true if you have two or more data files with different columns con taining the control channel values On the other hand if you have separate data files with the same column containing the control channel values you may use the general object name given below rather than entering the column number for the control channel values in each file ReferenceColum
110. 0 box Click OK 3 SelectExperiments gt Change Experiment Interpretation The Change Interpretation window will appear 4 Click the box marked Use Global Error Model 5 Click Save to save as part of your current interpretation or Save As to create a new inter pretation Copyright 1998 2001 Silicon Genetics 2 27 Creating DataObjects in GeneSpring Global Error Models Technical Details The two component model for estimating variation from control strength is known as the Rocke Lorenzato model The two components are an absolute error component that dominates at low measurement levels and a relative error component that dominates at high measurement levels The formula for the error model for raw pre normalization expression levels can be written as Gpp a tb S where paw 1S the measurement standard error of the raw expression data S is the measurement level control strength and a and b are the fitted coefficients of the model Expressed in terms of the normalized expression levels which are the result of dividing raw expression levels by control strength the standard errors can be written as G paw ja b S Before fitting the error model the genes are ordered by their control strengths A median variance and median control strength is calculated for each non overlapping set of eleven genes If repli cates are used this variance is the standard error of the samples in the current condition If the devia
111. 001 Silicon Genetics 3 28 Viewing Data in GeneSpring View as Spreadsheet View as Spreadsheet Allows you to view your data as a spreadsheet The spreadsheet color scheme and gene list reflect what is showing in the genome browser at the time you activate the new window The order of the genes is the same as in your Master Table of Genes S Yeast cell cycle time series no 90 min Default Interpretation V Shownormf Showcontrd ShowRaw Showttest Show flags Copy Selected Clear selectio time 0 minutes time 10 minutes time 20 minutes time 30 minutes time 40 n hormalized normalized normalized normalized norma 1 353 1 314 0 15 1 052 _YKLO42W 1 00 Figure 3 15 Spreadsheet View of the Similar to CLN1 list To Copy a Row for Pasting into another Document 1 Click on the row you wish to copy 2 Right click on the row and select Copy To copy the entire spreadsheet click the Copy A11 button at the top right corner of the spread sheet Note that if you have any rows selected you ll first have to click the Clear Selection button also in the top right corner of the spreadsheet To Locate a Particular Gene 1 Type Ctrl F 2 Type in the gene name 3 Click OK Inspect Found Gene To bring up the Gene Inspector for your found gene type Ctrl I Copyright 1998 2001 Silicon Genetics 3 29 Viewing Data in GeneSpring Linked Windows Linked Windows Allows you to select one gene or gene list in two windows
112. 01 Silicon Genetics Installing a Genome from a Text File The genomedef File Appendix l 8 Copyright 1998 2001 Silicon Genetics Installing from a Text File Define Your Experiment Appendix J Installing from a Text File This is possibly the most tedious and unforgiving of the experiment loading methods However it is necessary to be at least slightly familiar with the methods as you will need to change the exper iment file or re enter your experiment through another method when you need to make changes to the experiment Generally an experiment file is a text file describing where the data file s are what their format is what the parameters for the experiment are and what normalizations need to be done You can also specify pictures to be associated with the files and various other things Each line in an experiment file is either blank or a line of the form object name space colon space object value Object name object value An example of this is name Yeast extraterrestrial studies Obviously name is the object name and Yeast extraterrestrial studies is the object value The object value can be thought of as the answer to the question posed by the object name In the experiment file the order of lines is not significant but the case lower or upper case of letters is significant The spelling especially of the object name is also significant Usually when an experiment looks like it is not installed correct
113. 117 YMR200W 1384 MR201C 1101 Y MR202W 1357 YMR203W 1162 YMR204C 1464 YMR206W 978 YMRI99W 973 YMR207C 1618 YMR208W 1374 YMR209C 1432 MR210W 1068 YMR211W 1568 gt YMR199W 1313 MR212C 1638 YMR213W 1648 YMR214W 1282 gt Y MR199W 1218 gt Y MR199W 1496 GeneSpring averages all of the measurements of YMR199W to get an average control strength of 1286 GeneSpring notices the maximum control strength for YMR199W in this sample is 1496 Appendix G 16 Copyright 1998 2001 Silicon Genetics Normalizing Options Dealing with Repeated Measurements and the minimum is 1117 These values are the end points of YMR199W s error bar which Gene Spring will plot when you choose to display error bars in either the graph or the scatter plot dis plays After this average has been taken GeneSpring discards any measurements between the end points Hence the measurements 1313 and 1218 will be automatically discarded Measurement Flags Measurement flags are markers in your data set indicating whether or not any given measurement is regarded as Passed or OK Marginal Absent or Failed Data is assigned one of four flags Flags assigned by you when the experiment in entered into GeneSpring e Good Data data is present and reliable Marked with a P for passed or O for ok e Marginal Data data is present but of unknown or dubious quality Marked with an M for marginal e Absent Data there is no
114. 3 37 3 Double click the experiment name in the Correlations box at the center of the window This will bring up the New Correlation box with the default settings of that experiment This is the same box you would see if you added a new experiment to this correlation In the phase offset section you will need to select a parameter from the drop down list You will also need to enter a number to offset from What number you will enter depends on what makes sense with your chosen parameter 4 Click OK This will return you to the previous window the Multi Experiment Correlation win dow Click the Make List button in the upper right corner of the window GeneSpring will now look for genes with a similar shape to the inspected gene but offset accord ing to your input When GeneSpring has found genes whose profiles were similar but offset from your inspected genes a New Gene List window will appear Use the New Gene List window to name and save your new list This feature can be used if you want to see what genes might have triggered activ ity Copyright 1998 2001 Silicon Genetics 4 18 Analyzing Data in GeneSpring Making Lists from Properties Making Lists from Properties You can make gene lists based on the properties annotations contained in your Master Gene Table Such lists are not ordered To Make Lists from Properties 1 Select Annotations gt Make Gene List from Properties pre 4 1 users select Tools gt Make Gene L
115. 3 could be d txt as the differing numbers of samples in each file implies a different number of columns and therefor a different layout If you have more than one data file with differing column layouts you will have to repeat all of the subsequent panels dealing with locating which column contains what information for each data file you name When you right click the table in this panel of the Experiment Wizard there is no pop up menu allowing you to cut and paste You can still cut and paste entries into the matrix fields by using the keyboard commands for windows this is Ctrl C and Ctrl V If you right click one of the gray areas of this table a copy and paste pop up menu will appear These pop up menus allow you to cut and paste large sections of the table Once you have filled in every field in the table you can proceed to the next panel by clicking on the Next button You may see a quick flutter of an error message if GeneSpring cannot find the cor rect folder in your directory Look in the TaskBar if GeneSpring will not let you go to the next panel If an error message such as Oops Can t find the file appears use your file management system to create the correct folder and place a copy of your data file within it In this configuration of the Describe your Data Files panel you need to click in the Appendix D 7 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard beige box in the File N
116. 31 Changing the Coloring Scheme nssssnsesssesessseseesresseessesssstessesersseessessrssessressesees 3 31 2 Copyright 2000 2001 Silicon Genetics Color By Expressions iii isians dit dta des ev Avia nd Aussies Maas Coa aes tae Andee 3 31 Color BY Significante sotare aE a aran EEE REAN ces ERER EATS LEEKS R ARES 3 33 Color by Static Experiment ssssesseseseseessessesresseesesresseeseesresstessessrssressessessees 3 33 Color by Venn Diagpr mi crcsree ata eea EE N aai 3 33 Color by Parameter oanrin ea E R RAO aes eee 3 33 No 0 Lo sapere ee ee en a a a E A E E E aeeee 3 34 Color by Classification se taseren ori a a EE A E a a a 3 34 Color by Secondary Experiment cccsccssssssccssssssscssctescsestccssscssscssacessecenes 3 35 Changing the Experimental Data Range s sessessesesssesessesseseesesseseesesersessesesse 3 36 Changing the Default Colors ssssssssessssessessseseesseeseseesseeseeseesseessesersresseesseseess 3 37 The Inspectors nicas a e E E E E S A nasa staat 3 37 Gene INSPECTION desinen eiat En AA AT EE E A EARNER ARTT 3 37 Experiment and Condition Inspectors cccccecccecsceeeseceteceeeeeeseeeeeeceteeeeeeenseees 3 41 Condition Spectre nocri e o E E T A EA A E AE leonns 3 43 Eist HIS BE COR maene e aa dood a a e a A a a 3 44 Classification Inspector cessen ueni a awed a a des 3 46 Chapter 4 Analyzing Data in GeneSpring e seeessoessoesssesssesssoossoossoossssssssessoossosssosssssee 4 1 Filter Gen
117. 4 Analyzing Data in GeneSpring for more details From the Gene Inspector window you can do the following e Making Lists with the Find Similar Command The Find Similar command allows you to create a list of genes having similar expression profiles to the gene being displayed See Making Lists with the Find Similar Command on page 4 13 for more details e Making Lists with the Complex Correlation Command The Complex Correlation Com mand allows you to make a list of all the genes satisfying various conditions you define See Making Lists with the Complex Correlation Command on page 4 14 for more details Many other tools are available with which you can make lists e Making Lists with the Venn Diagram Select Colorbar gt Color by Venn Dia gram to begin Right clicking over lists in the navigator will allow you to fill the diagram This function allows you to make lists based on the membership of genes in a Venn Diagram See Making Lists with the Venn Diagram on page 4 19 for more details e Making Lists with the Filter Genes Command Select Tools gt Filter Genes It allows you to use expression level constraints and control strength restrictions to create a smaller gene list See Filter Genes Analysis Tools on page 4 1 for more details e Making Lists from Selected Genes You can make a list of all the genes you have selected in the genome browser by right clicking and choosing Make List from Selected Genes
118. 9 Experiment 0 50 Classificatic Pathways ga Array Layout 010 Drawn Gene External Pro Bookmarks 0 30 Scripts pS F ta 30 00 0 0x Mm PCA component 1 0 00 0 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 0 90 Trust day 11 Embryonic Animate Magnification 1 Zoom Out hi ea Figure 5 2 PCA Scatter Plot in Log Mode You can change the components that are represented by each axis by right clicking one of the gene lists in the PCA gene list folder Copyright 1998 2001 Silicon Genetics 5 6 Clustering and Characterizing Data in GeneSpring Viewing Principal Components in an Ordered List Principal Components Analysis Perhaps the best way to visualize the genes that exhibit the highest levels of an individual compo nent is to use the ordered list view Select View gt Ordered List and select one of the PCA gene lists from the navigator panel Genes exhibiting the highest levels of the selected principal component will be displayed on the left side of the genome browser and will have the longest lines extending upward from them For more details please see Ordered List View on page 3 21 S GeneSpring 4 1 Rat Genes PCA component 1 File Edit View Experiments Colorbar EJ Gene Lists _ Classification 1 _ Classification 2 J PCA Rat Study H PCA component 1 PCA component 2 _ PCA test all genes interesting genes Experiments Gene Trees Experiment Trees C
119. 98 2001 Silicon Genetics Genome Wizard When you right click the table in this panel of the Genome Wizard there is no pop up menu allowing you to cut and paste You can still cut and paste URLs into the matrix fields by using the keyboard commands for Windows this is Ctrl C and Ctrl V Cutting and pasting has a much higher success ratio as URLs are both spelling and case sensitive GeneSpring will attempt to locate each URL you insert before it allows you to proceed to the next panel This may be a problem if you are not connected to the internet when you are creating this genome If this is the case you will have to skip this panel and add the web links to the genomedef file later To add hyperlinks from GeneSpring please see Searching Internet Databases on page 3 40 For NT and Mac users you should set the path to your usual browser because GeneSpring can not automatically locate the default web browser on NT or Mac machines which may cause you trouble in this panel To set the path to the browser a SelectEdit gt Preferences b Select Browser from the drop down menu c Inthe Browser path box either type the complete file name and pathway of the exe file for your default browser or click the Browse button to the right of the Browser path box If you do this a window will appear The default from the Preferences box may take you into the wrong folder You will need to look for your default browser s files in your syste
120. Applicable only to Graph view the Continuous Element mode shows parameter values existing on a continuum where each point is connected with a line GeneSpring automatically orders numerical parameters from highest to lowest and non numerical parameters in alphabetical order See Parameter Display Options on page 2 12 for details Non Continuous Applicable only to Graph view Non continuous mode shows parameter values existing indepen dently of one another where each value is represented as a discrete point GeneSpring automati cally orders numerical parameters from highest to lowest and non numerical parameters in alphabetical order See Parameter Display Options on page 2 12 for details Replicate This mode applies to one of several experimental scenarios in GeneSpring e When you have one sample split across more than one chip e When you have multiple samples representing the same state e When samples from multiple tissues represent the same state Parameter defined as replicates are averaged together and appear as a single parameter Note that when the same gene occurs twice in the course of an experimental set it is called a repeat and the measurements are averaged together This cannot be changed Copyright 1998 2001 Silicon Genetics 2 20 Creating DataObjects in GeneSpring Experiment Normalizations Color Code The Color Code mode colors genes by parameter the number of times a single gene is drawn is equ
121. CGT in all ORFs From 10 To 500 bases upstream of each gene L3 before CLN1 Ls High 40 From 5 To 8 length oligonucleotides 5 like Drawn Gene 0 95 3 like YMR199 V CLN1 0 Allow Ns in Regulatory Sequence a like YMR199W CLN1 0 From 0 To 0 single point discrepancies 2 ONCO_PREDICT xi From 0 To 0 Ns in the exact middle rStatistics Relative to upstream of other genomic elements Relative to whole Genome Probability Cutoff 0 05 Progress Start ic Help Figure 4 5 The Regulatory Sequences window Copyright 1998 2001 Silicon Genetics 4 26 Analyzing Data in GeneSpring Regulatory Sequences To find a Potential Regulatory Sequence 1 Select Tools gt Find Potential Regulatory Sequences The Find Potential Regulatory Sequences window will appear Select a gene list from the Gene Lists folder in the mini navigator of the window Note Do not choose the all genes or all genomic elements gene lists because you are already com paring your selected gene list against all other genes in the genome Choose Find new regulatory sequence orEnter a specific regulatory sequence from the pull down menu at the top center of the window e Find new regulatory sequence This option searches for short sequences upstream of the genes in the current gene list or across the entire genome e Enter a specific regulatory sequence This option allows you to enter a known sequence
122. Choose an experiment or a gene list from the navigator When you choose to copy and experi ment please be aware you will copy only the gene list currently selected If you want to copy all the genes in your currently viewed experiment please right click over the All genes list and select Display List before you begin to copy In the main GeneSpring window select Edit gt Copy gt Copy Experiment Your data will be saved to the clipboard From there you can paste your experiment or gene list into Microsoft Notepad Microsoft Word or Microsoft Excel When you paste the gene list will be sorted into the order presented in the Ordered List view Appendix F 4 Copyright 1998 2001 Silicon Genetics Normalizing Options Appendix G Normalizing Options To normalize in the context of DNA microarrrays means to standardize your data too be able to differentiate between real biological variations in gene expression levels and variations due to the measurement process Normalizing also scales your data so that you can compare relative gene expression levels GeneSpring offers the following normalization options There are several normalization options available in GeneSpring e Normalize to Negative Controls also referred to as Background Subtraction e Normalize to Control Channel Values for Each Gene also referred to as Per Spot normaliza tion e Normalize to Positive Controls e Normalize Each Sample to Itself also referred to
123. Enter the number of bases upstream of each gene you would like to search in the Search Before ORF section of the window For example if you enter From 10 To 100 on a search for ACGCGT GeneSpring will search for any part of the promoter within the region between 10 and 100 The smaller the range between these numbers the more likely the results will be statistically significant Larger sequences may take longer to search You can also search for common sequences within the ORF by using negative numbers for the bases e Enter the length of the oligonucleotides to search for if you have selected the Find new regulatory sequence option in the first step e Enter the promoter sequence in the Enter Sequence textbox if you have selected Enter a specific regulatory sequence in the first step Enter the number of single point discrepancies allowed in the textbox provided This refers to a maximum number of mismatches allowed i e if you specify 1 single point discrepancy then ACGCGAT satisfies a search for ACGCGTT Enter the range of base gaps in the exact middle if you have selected the Find new regu latory sequence option in the first step This refers to the size of an allowable hole in the middle of the sequence allowing you to look for sequences such as ACGnnnCGT which is biologically relevant due to loops and non binding areas The gap must be in the exact mid dle with the longer side of odd sequences appearing before the Ns The gap does
124. Experiment File Formats e ssseeesooessossssesssesssocssoossossssesssoessoossoossosssssee K 1 Wea Data dct ot 9 erena a a eae dt E a aa E a a ooh K 1 What format does this data need to be in oo ee eee ceseceseceseceesceceeceseceeeeeeseecaeenees K 2 Experimental D ta cie eraan aiea Ea E Ea REE RE aE ATE ewes K 2 Pictures of the conditions during the experiment ss sesssseseesessssesseseresressesre K 2 Pictures of the Microarray plates 35 ic213 sancsecsendvens cases esseedneainseeae retained K 2 AVC A Ay OUT Merr omera ys aelou av e vate a va scales seacatan vases hiatus me K 2 The Region Designation File s vtsiece icecataeeiive teil aaa exes wd Saves K 4 Entering region specifications when they are not specified in their own column or as suffixes within another column ceeeeeeeees K 5 How to describe a Map eunis ie tases di sauaea ni Ee aE AESA A EES K 7 The Positive and Negative Control Files cccceccccssceessecsseceeeceeeeeeseecsseenseeeees K 7 Wherteido Iput my Oa el ctag en Soh sa elses See tnciia a heat e a AEA S K 8 Appendix L Equations for Correlations and other Similarity Measures L 1 Common Correlations sken a Waa wlal thea E Td L 2 Standard Correlation oicsnnnesi sneaiceanna ai iiaa L 2 Pearson COREG ATOM mnnn enaa eaaa aeae A RETE ER ERa ania L 2 Spearman Correlation ssssssessesseessessessessressessesstesseesessresstesesresstessesressessesstssres L 3 Spearman Confidence
125. G 12 Copyright 1998 2001 Silicon Genetics Normalizing Options The results of normalizing each sample to itself Normalizing All Samples to Specific Samples After Normalizing Each Sample to Itself Treated Samples Controls Tissue X Tissue Y Tissue Z et a es Gene Name Sp 1 Sp 2 Sp 3 Sp 4 Sp 5 Sp 6 Sp 7 Sp 8 Sp 9 CLN 1 l l 2 5 3 1 5 1 5 l l 1 5 CLN2 1 1 1 1 1 1 1 1 1 CDC28 0 1 0 1 0 5 0 5 0 5 0 1 0 1 0 5 l HSL1 1 1 4 4 2 2 1 4 2 YGPI1 15 10 20 20 10 10 10 20 10 Samples 1 2 and 7 are normalized to sample 7 and samples 3 4 and 8 are normalized to sample 8 and samples 5 6 and 9 are normalized to sample 9 Note that the normalized data for every gene in each of the three control samples will be 1 Appendix G 13 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalizing All Samples to Specific Samples After Normalizing Each Sample to the Control Sample Treated Samples Controls Tissue X Tissue Y Tissue Z pees pate pees Gene Name Sp 1 Sp 2 Sp 3 Sp 4 Sp 5 Sp 6 Sp 7 Sp 8 Sp 9 CLN 1 1 1 2 5 3 1 1 1 1 1 CLN2 1 1 1 1 1 1 1 1 1 CDC28 1 1 1 1 0 5 il 1 1 1 HSL1 1 1 1 1 1 1 1 1 1 YGP1 1 5 1 1 1 1 1 1 1 1 Another way to use this normalization method requires that your experiment be designed to have a set of controls that you wish to use en mass as the cont
126. G cat exe lt 3 del 1 lst 1 log 2 3 Pct Note When you are preparing this file remove the plus sign and combine the two lines beginning with C PROGRA 1 into one long line This batch file takes the standard input from GeneSpring stores it in a file executes SAS and then passes the results back to GeneSpring via standard output The program cat exe simply cop ies standard input into standard output if you do not have something equivalent on your system cat exe can be downloaded from Silicon Genetics web site GeneSpring data Fastclus sas filename infile sysget infile filename outfile Ssysget outfile proc import datafile infile DBMS TAB out experiment replace datarow 3 Oo getnames no run proc fastclus data experiment maxclusters 5 maxiter 50 out clusters keep varl cluster id varl run proc export data clusters outfile outfile DBMS TAB replace run This runs PROC FASTCLUS specifying 5 clusters In PROC IMPORT the datarow 3 command skips the first 2 lines of the exported data which contain the dataset name and one parameter If you have more than one parameter you should adjust the data row value accordingly PROC EXPORT puts a header line on the return data set listing the variable names and Gene Spring will give you an error message and should skip this line unless you have a gene na
127. GeneSpring User Manual version 4 1 Release date 27 September 2001 Copyright 1998 2001 Silicon Genetics All rights reserved GeneSpring GeneSpider GenEx GeNet and MicroSift are trademarks of Silicon Genetics All other products including but not limited to Affymetrix GeneChip Affyme trix Global Scaling GenBank Microsoft Excel Microsoft Notepad and Adobe FrameMaker are the trade marks of their respective holders Related Documents GeneSpring Basics Instructional Manual version 4 0 2 Release date 31 May 2001 GeNet User Manual version 2 3 Release date 12 June 2001 Table of Contents Chapter 1 Introduction sascisssscsaciiateassceewchccasscSeudosucsacsdeccvensstecoxsnseancsuncasbateseoaeesaneavseauncs 1 1 Getting Started naa a e a A a E E A E E ERNER 1 1 Learning to Use GeneSpring assis tacsasstcathetugs eau aw inane eee eons 1 3 New CU Version AU en erence teu toas canis sha cau care ane a eal eth ntnadealaaet 1 4 GENES Prine Basics aenn ien a enan oE ia A wantin ave E T as 1 7 The GeneSpring Hierarchy of Objects or Where Is My Data Stored 1 15 Commonly Used GeneSpring Functions ccccecceeceeesceeseeeeeceeeeeseecaecneeeneeeensees 1 17 The Gene Inspector window sciccictasiertans aca iatect aaah cmeade nice peassesteencadeats 1 17 Making EA SUS afte cag a cence ye a a ome vores gto A nate oe eR 1 17 Chapter 2 Creating DataObjects in GeneSpring e ssessseessoossoossoossssesssesssoossosso
128. Genetics 4 36 Analyzing Data in GeneSpring Creating Your own Scripts 5 Gene List Manipulation All Genes Result is All Genes list No inputs or knobs Output is All Genes Gene List All Genomic Result is All Genomic Elements list No inputs or knobs Output is All Genomic Elements Gene List Gene List Difference Make a Gene List of the genes that are in the first gene list but not the second gene list 2 Gene List inputs Output is a Gene List Gene List Intersection Make a Gene List of the genes that are in both input gene lists 2 Gene List inputs Output is a Gene List Gene List Union Make a Gene List of the genes that are in either input gene list 2 Gene List inputs Output is a Gene List In all Gene lists Make a Gene List of the genes in all the input gene lists 1 Gene List Group input Output is a Gene List In at least one Make a Gene List of the genes in at least one of the input gene lists 1 Gene List Group input Output is a Gene List Merge Gene List Group Make a Gene List of the genes in a certain proportion speci fied by knobs of the input gene lists 1 Gene List Group input Knobs for Percentage amp Comparison Output is a Gene List Number of Genes Produce the number of genes in the gene list 1 Gene List input Out put is a number number of genes in the gene list 6 GeNet Publishing a Default Directory Send Classification to GeNet Publish a classification to yo
129. Graph by Genes view 3 26 commands 3 26 Graph raw data P 6 Graph view 3 7 color by secondary experiment 3 35 Graphics Specifications D 15 Guess the rest D 11 H hard number G 7 headlines J 7 Help Menu About A 2 FAQ A 1 Manual A 1 SiG on the Web A 2 System Monitor A 2 Version Notes A 1 Hide All 3 6 Hierarchical Clustering View see Tree View homologous genes 4 31 Horizontal Label P 6 housekeeping genes 2 22 How to Display the Parameters D 5 I Import data by pasting F 1 from GeNet 6 8 Inspectors Condition 3 43 Experiment 3 41 Gene 3 37 Interpretation 3 41 installation files K 1 installing GeneSpring 1 1 Interpretation Inspector 3 41 interpretations 2 17 J JDBC driver B 1 Copyright 1998 2001 Silicon Genetics K KEGG 4 25 Keywords H 3 K means clustering 5 9 Maximun Iterations 5 11 Number of Clusters 5 11 Kyoto Encyclopedia of Genes and Genomes 4 25 L layout file K 2 negative controls J 15 positive controls J 16 region specifications J 9 List Inspector 3 44 Lists Find Interesting Genes 4 21 Find Similar 4 13 from annotations 4 19 p value 4 11 Regulatory Sequences 4 29 Venn Diagram 4 19 Load Sequence P 5 command 3 13 M Magnification 3 6 Main GeneSpring Screen see Browser display Make New Tree 5 1 Mapped format K 7 Common Name H 2 custom H 3 EC Number H 2 function H 3 GenBank Accession Number H 3 gene list formats H 2 Keywords H 3 Map H 2 phenotype H 3 Protein Product H 3 Public Medline accession number
130. H 3 sequence H 3 Systematic Name H 2 Mapping information H 2 Master Gene Table 2 15 C 3 H 1 gene list formats H 1 mathematical notation L 1 measurement flags D 11 G 17 J 12 Abs Call 2 17 Index 3 memory 1 2 Minimum Distance 5 3 5 4 missing expression values L 1 mock phylogenetic 5 2 Multi Experiment Correlation 4 14 N name function H 2 gene list formats H 2 name list H 1 gene list formats H 1 Navigator 3 6 negative control strengths G 18 Negative Controls J 14 new Pathway 4 24 nodes 5 12 non continuous parameter J 4 normalization options 2 21 All Samples to a Specific Sample D 15 All Samples to Specific Samples G 10 all samples to specific samples 2 25 background subtraction 2 21 constant value 2 24 Control Channel Values D 13 Control Channel Values for Each Gene G 3 Distribution of All Genes G 6 distribution of all genes 2 23 Each Gene to Itself D 15 G 8 Each Sample to a Hard Number D 14 G 7 Each Sample to Itself D 14 G 6 gene to itself 2 25 Global Scaling G 6 hard number 2 24 Negative Controls 2 21 D 13 G 2 order 2 21 per chip 2 22 per spot 2 22 positive control 2 22 Positive Controls D 13 G 5 pre normalized data 2 24 Region Normalization G 15 normalization techniques G 1 Normalization to Specific Samples G 10 Number of Arrays D 4 J 1 Number of Parameters D 5 J 2 O ODBC E 1 one color experiments 3 32 opening new genomes 1 17 Copyright 1998 2001 Silicon Genetics Options Change Vertical Axis Range
131. Mathematical Illustration the Normalize to Positive Controls Method Given raw data with positive controls Raw Experimental Results Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1000 2000 1500 CLN2 1000 2000 500 CDC28 100 200 50 HSL1 1000 2000 500 YGP1 10 000 20 000 5000 Control 1 5000 10 000 2500 Control 3 2000 4000 1000 The results of normalizing to positive controls After Normalizing to Positive Controls Gene Name Sample 1 Sample 2 Sample 3 CLN 1 0 5 0 5 1 5 CLN2 0 5 0 5 0 5 CDC28 0 05 0 05 0 05 Appendix G 5 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalize Each Sample to Itself After Normalizing to Positive Controls Gene Name Sample 1 Sample 2 Sample 3 HSL1 0 5 0 5 0 5 YGP1 5 5 5 See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring Normalize Each Sample to Itself This normalization is intended to remove the differences in amount of exposure between samples so different samples are comparable to one another This method makes the median of all of your measurements 1 for each sample The formula used to do this is the signal strength of gene A in sample X the median of all of the measurements taken in sample X This normalization should not be used with normalizing to positive controls as they are
132. OK To remove an experiment or condition click on the experiment or condition and select Remove Specify boundaries correlation coefficients for what is considered similar in the Maximum and Minimum boxes Copyright 1998 2001 Silicon Genetics 4 14 Analyzing Data in GeneSpring Making Lists with the Complex Correlation Com mand 6 Choose a correlation from the drop down menu For more information about correlations see The Correlations box on page 4 16 and Equations for Correlations and other Similarity Measures on page L 1 The Restrictions box at the bottom of the window specifies the restrictions the genes have to pass before they reach the correlation stage To add restrictions to the selected list right click an experiment or gene list in the navigator and select a restriction For information on restric tions and how to apply them see Filtering Genes on page 4 1 Select the Make List button to make a list and keep the Multi Experiment Correlation win dow open or the OK button to make a list and close the window A New Gene List window will appear This window lists all the genes in your new list as well as similar lists with their associated p value Name your gene list and click Save The list will show up in the Gene Lists folder of the main navigator The Multi Experiment Correlation Window S Multi Experiment Correlation against YMR199W CLN1 loj x HI Gene Lists Gene List
133. OT L ALE A OE AULA 1 M02 O00 ERY QUANTO UDU OOUR A E A PIL EEA AA A TA AN LN A A Ti AN YA A time 0 minutes Magnification 1 so on wn oO OoxKM Animate Zoom Out sl Figure 3 14 The Classification View Each gene is divided up according to the gene lists in the Gene Onology Function subfolder with the genes listed below their classifications It is not surprising given the source of the classifica tion that there are many cell growth and maintenance genes You could choose any other gene list to view by selecting it in the navigator Once fully zoomed in you can easily see the individual genes as small distinct rectangles You can zoom in to see some genes in greater detail The gene names and the sequence will appear when there is enough space It is possible for a single gene to be in more than one group in which case it will be displayed in the first vertical group it is in Genes not mentioned in any of the gene lists end up in the unclassified section on the bottom The unclassified classification is a list of genes actively specified as unclassified Some classifications may contain no genes depending on the list you are currently viewing To clear a classification and return the genome browser to the unsorted state right click over the Classifications folder in the navigator and select Clear Classification from the pop up window Copyright 1998 2
134. Postna tal or Adult could be parameter values of the experiment parameter stage while 01 ppm could be a parameter value of the experiment parameter concentration What is meant by Replicates Replicates can be multiple spots on the same array representing the same gene also referred to as a copy the same sample on more than one array or a biological replicate that is equivalent sam ples taken from more than one organism Graphically a parameter defined as a replicate is a hid den variable no visual distinction is made based upon this parameter or its parameter values What is meant by Raw Data The analysis process begins by obtaining data in the form of flat files that were generated by your scanning software or other expression analysis technology GeneSpring is capable of recognizing most commercially available formats and can learn to recognize initially unfamiliar formats as they arise Typically the gene spot probe set intensity values in these files are referred to as raw data What is meant by Normalized Data If GeneSpring recognizes your file format it will apply a set of default normalizations appropriate for your expression analysis technology The denominator used to normalize each measurement is referred to as the control strength What is meant by Interpreted Data GeneSpring is able to interpret normalized data in many different ways You can elect to have multiple samples treated as replicates and averaged an
135. Right click a subfolder and select Use as Coloring 3 Right click an existing classification in the Classifications folder and choose Set as col oring scheme For more information on coloring see Changing the Coloring Scheme on page 3 31 You can also see how many genes have no data by noting how many genes are greyed out If you switch to other views you can return via View gt Classification automatically selected by classifying a list using the methods above Note If you select Classification from the View menu without specifying a classification method the genome browser will display the genes without any classification Copyright 1998 2001 Silicon Genetics 3 9 Viewing Data in GeneSpring Physical Position View Physical Position View This Physical Position display allows you to see an experiment or a set of experiments by organiz ing the genes according to their physical position when the gene loci are known and loaded into GeneSpring within the DNA sequence the organism Select View gt Physical Position The Physical Position view works for any organism whose mapping data is at least partially avail able An illustration of what Physical Position View looks like for humans is given Figure 3 5 For organisms already sequenced the physical position views will look more like yeast illus trated in Figure 3 4 gt GeneSpring 4 1 Yeast Genes all genomic elements l Fie Edit Yiew Experiments Colorbar Tools Annotati
136. Select Condition Selects 1st Condition if Boolean is True and selects 2nd Condition if Boolean is false 1 Boolean input amp 2 condition inputs Output is a Condition Select Experiment Selects 1st Experiment interpretation if Boolean is True and selects 2nd Experiment interpretation if Boolean is false 1 Boolean input amp 2 Experiment inter pretation inputs Output is an Experiment interpretation Select Experiment Tree Selects 1st Experiment tree if Boolean is True and selects 2nd Experiment tree if Boolean is false 1 Boolean input amp 2 Experiment tree inputs Output is an Experiment tree Select Gene Selects 1st Gene if Boolean is True and selects 2nd Gene if Boolean is false 1 Boolean input amp 2 Gene inputs Output is a Gene Select Gene Classification Selects 1st Classification if Boolean is True and selects 2nd Classification if Boolean is false 1 Boolean input amp 2 Classification inputs Output is a Classification Select Gene List Selects 1st Gene List if Boolean is True and selects 2nd Gene List if Boolean is false 1 Boolean input amp 2 Gene List inputs Output is a Gene List Select Gene Tree Selects 1st Gene tree if Boolean is True and selects 2nd Gene tree if Boolean is false 1 Boolean input amp 2 Gene tree inputs Output is a Gene tree Select Number Selects 1st Number if Boolean is True and selects 2nd Number if Bool ean is false 1 Boolean input amp 2 Number inputs Output is a N
137. Selecting this will result in a caution window asking you to verify the deletion of the interpretation Click Yes to delete The Classifications Subfolders Pop up Menus A right click over a classification will bring up the following commands Set As Classification This command allows you to apply the classification system of that folder to whatever list your are currently viewing Please see Classifications View on page 3 9 for more details Set As Coloring Scheme This command allows you to use a set of classifications as a color ing scheme Each set will be assigned a color and will display in that color by GeneSpring Please see Color by Classification on page 3 34 for more details Split Unsplit Window This feature allows you to view multiple graphs or any other display type simultaneously in the genome browser You can also unsplit the window by selecting View gt Unsplit window Make Gene Lists With this command you can make a list of a classification The New Gene List window will appear asking you to choose create a folder and name your new list Inspect This will bring up a window with the administrative information associated with this experiment You can click the Edit button to change most of the information presented in the Inspect window Common Commands in the Experiment Specification area While there are no new commands available by right clicking in the experiment specification area there are several
138. Splitting and Duplicating Experiments on page 2 6 for more information You can also use this feature to copy an experiment e Change Experiment Parameters This command allows you to add new change or delete various parameters from your experiment Please refer to Normalizing Options on page G 1 for more information e Experiment Normalizations This command allows you to change the normalization tech nique used on your experiment For an overview of the possible normalizations please refer to Normalizing Options on page G 1 e Change Experiment Interpretation With this command you can change various aspects of the displayed experiment for more details please see Changing the Experiment Interpreta tion on page 2 17 The Colorbar Menu You can change any of the default colors used in the genome browser For more information please refer to Preferences Window on page B 1 You can also right click over the colorbar to change the range of brightness trust of the colors e Color by Expression Current Experiment Selecting the first command in the list will return you to the default coloring for your current experiment Please refer to Color by Expression on page 3 31 for more details on this topic e Color by Significance Please refer to Color by Significance on page 3 33 for more details on this topic e Venn Diagram This command allows you to assign various gene lists to colored circles with
139. Spring Basics Alternatively select File gt Manual Load Experiment gt Experiment Import Wizard Follow the instructions on each screen until your experiment is loaded For more infor mation on using the Wizard see The Experiment Wizard on page D 1 Step 3 Assigning Normalizations Parameter Values and Interpretations a SelectExperiments gt Experiment Normalizations Choose the types of normalizations to apply Four classes of normalizations are available background subtraction per spot normalizations per chip global normalizations and per gene normalizations Spec ify normalizations and save For information about normalizations and when to apply them see Experiment Normalizations on page 2 21 b SelectExperiments gt Change Experiment Parameters Set parameter units values value order and add any missing parameters For information about changing experiment parameters see Change Experiment Parameters on page 2 8 c SelectExperiments gt Change Experiment Interpretation Select the mode of display lower and upper bounds of data the flagged measurements to be included whether to use the Global Error Model whether the data should be continuous non continu ous viewed as a replicate or color coded Note that these assignments are an extremely impor tant preparation for any type of data analysis For information about changing experiment interpretations see Changing the Experiment Interpretation
140. T Figure 3 1 Example of a k means clustering In Figure 3 1 the example represents a k means clustering colored by expression values Note the list name and number of genes shown in the upper right corner of each small screen In this instance the names are set numbers from the original k means clustering To Split a Window 1 Right click a gene list folder or classification in the navigator and select Split Window A submenu will appear 2 Select from one of the following display options e Horizontally to divide the window into columns e Vertically to divide the window into rows Both to create a grid To unsplit a window select Split Window gt Neither or View gt Unsplit Window 3 3 Copyright 1998 2001 Silicon Genetics Viewing Data in GeneSpring Finding and Selecting Genes g Displaying a Gene List To display a gene list 1 Right click on the gene list you wish to view in the Gene List folder in the navigator A sub menu will appear 2 Select Display List Displaying a Gene List as a Secondary List 1 Display a gene list as outlined above then right click above the gene list you wish to view as your secondary gene list A submenu will appear 2 Select Display As a Second List To remove the secondary gene list go to the View menu and select Remove Secondary Gene List Finding and Selecting Genes The Find Gene function allows you to quickly find a gene when using a view where individual ge
141. Technical Details on the Statistical Group Comparison For Each Gene Parametric Test Variances Assumed Equal For parametric test with variances assumed equal compute e _ DNX X 1_____ the overall mean G gt N i l G ZO 2 BSS gt N X X the between groups sum of squares i l d G 1 the numerator degrees of freedom BMS BSS the between groups mean square 1 G WSS gt SS the pooled within group sum of squares inl G d S N 1 the denominator degrees of freedom ial if is not greater than zero then exit p value 1 WMS WSS A the within groups mean square and 2 F BMS gg the F ratio statistic if WMS 0 then make F 1s treated as arbitrarily large p value 0 The p value is calculated by looking up F inthe upper tail probability of an F distribution with d and d degrees of freedom Parametric Test Variances Not Assumed Equal For the parametric test without assuming variances equal First check that each group has N greater than or equal to 2 and SS greater than 0 if not remove it from consideration and recompute G again If G is not at least 2 exit p value 1 1 This reflects the more stringent requirements of not assuming the variances equal if the variance esti mate is pooled replicates are only needed for at least one group if variances are separately estimated then replicates are needed for each group Copyright 1998 2001 Silicon Genetics Appe
142. These raw values can be seen in the upper right table in the Gene Inspector Copyright 1998 2001 Silicon Genetics Appendix Q 2 Glossary Menu pull down options that allow you to perform tasks in GeneSpring The main menu can be found at the top the main GeneSpring window PC or at the top of your screen Mac N Navigator the left panel of GeneSpring windows containing data organized into folders Normalize the use of statistical methods to eliminate systematic variation in microarray experi ments that can influence measured gene expression levels P Panel section of a window or screen Pathways A pathway is a graphical representation of the interaction between gene products in a biological system Genes can be superimposed on the pathway allowing you to view their expression levels in a biological context Parameter Value one of the possible values assigned to a variable For example in the equation X 1 2 3 or 4 X is the experimental parameter and the numbers 1 2 3 or 4 are each a different parameter value of X A more pertinent example is the parameter values breast cancer kidney cancer liver cancer brain cancer and no cancer could all be different parameter values for the exper imental parameter cancer Parameters Color Code is similar to a discrete parameter except you would expect points on a graph with the same parameters other than this one to be at the same horizontal position Colo
143. a list of genes and allow you to select one by clicking on the name or save the list as a gene list You can also bring up the Find Gene window by typing Ctrl F Undo Edit gt Undo will undo your last action The Undo command has some memory so you may be able to undo several actions You can also Undo by typing Ctrl Z Preferences This window will allow you to change many of the default settings in Gene Spring including the colors used to display the genes For more information please refer to Preferences Window on page B 1 Copyright 1998 2001 Silicon Genetics Appendix P 2 Common Commands Common Commands in the Drop Down menus The View Menu In the View menu are all the display options you may choose for your data e Unsplit Window The Split Window command allows you to view multiple graphs simulta neously in the genome browser To split the window right click over a Gene Lists folder or a classification in the navigator and select Split window from the pop up menu Unsplit Win dow allows you to undo that feature and return to a normal screen e Visible Under this command is a submenu presenting you with what you may show in your current view If you are trying to maximize the screen you can turn all the options off The Experiments Menu e Merge Split Experiments This command allows you to merge data from several experi ments into one or to split data from one experiment into several Please refer to Merging
144. al to the number of parameter values defined as conditions allowing you to easily compare varying conditions of the same gene By default parameter values are listed in alphabetic or numerical order See Parameter Display Options on page 2 12 for details Experiment Normalizations To normalize in the context of DNA microarrays means to standardize your data to be able to dif ferentiate between real biological variations in gene expression levels and variations due to the measurement process Normalizing also scales your data so that you can compare relative gene expression levels GeneSpring assumes that the data that you have entered is raw data that needs to be normalized Note that if your data has been pre normalized around a median other than 1 it may not be inter preted accurately during analysis If your data is pre normalized this way please refer to Use Constant Values on page 2 24 or Normalizing Each Sample to a Hard Number on page G 7 There are several ways to normalize your data in GeneSpring Typically you will want to do either one per chip normalization together with one per gene normalization or one per spot nor malization with one per chip normalization There are important exceptions to this which are dis cussed below under the relevant normalization Note also that the order in which normalizations are performed is mathematically significant GeneSpring performs them in the order in which they are
145. alize to negative controls you probably want to either normalize to positive con trols or each sample to itself and then normalize each gene to itself Mathematical Illustration of the Normalize to Negative Controls Method Given the raw data with negative controls Raw Experimental Results Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1008 2060 1510 CLN2 1008 2060 510 CDC28 108 260 60 HSL1 1008 2060 510 YGP1 10 008 20 060 5010 Control 1 7 58 10 Control 2 8 60 0 Control 3 9 63 20 Copyright 1998 2001 Silicon Genetics Appendix G 2 Normalizing Options Normalize to Control Channel Values for Each Gene The same data normalized to negative controls After Normalizing to Negative Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1000 2000 1500 CLN2 1000 2000 500 CDC28 100 200 50 HSL1 1000 2000 500 YGPI1 10 000 20 000 5000 Median of the Controls 8 60 10 See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring Normalize to Control Channel Values for Each Gene Control Channel Values are intended to provide a baseline Different samples can be compared to the baseline and to one another By using these comparisons you can determine variations caused by the particular experimental conditions you are exploring rather than the overall sample condi tions If you have a contr
146. alled Find Genes Which Could Fit Here can be used as a tool to predict new pathway elements G The Array Layouts folder contains information about the arrangement of the spots on your array These can be used to recreate an image of your arrays to check for regional abnormali ties H Drawn genes are lines representing gene profiles that you draw in the genome browser You can then search for genes matching that profile Any drawn genes you create are stored in the Drawn Genes folder I External programs are analysis programs outside GeneSpring that can be launched from within GeneSpring Data from GeneSpring is sent to the program and output from the pro gram is recognized by GeneSpring These programs are kept in the External Programs folder J Bookmarks are saved display settings such as experiment gene list color scheme selected genes etc You can always save your current display and return to it later by opening the Bookmarks folder and selecting a particular bookmark K Scripts are tools that save time by allowing a long series of data analysis steps to be per formed at once Scripts are re usable and can be applied to any data set You can create your own scripts using Silicon Genetics Script Editor All scripts including complimentary scripts shipped with GeneSpring 4 1 are stored in the Scripts Folder By default folders in the navigator are closed although on start up GeneSpring displays an all genes or all geno
147. alling a Genome from a Text File The genomedef File 16 You can place any data you wish in the custom label columns CustomlLabel heading CustomlLabel interacts with P53 17 You can place any data you wish in the custom label columns Custom2Label heading Custom2Label molecular weight 18 You can place any data you wish in the custom label columns Custom3Label heading Custom3Label plate and well location 19 If your genome has a unique identifier such as a nickname that would speed searching for it enter it in this line Identifier optional unique identifier for the whole genome Identifier dutch elm disease study 20 You can use ChromosomeNames to cause the mito chromosomes to be sorted separately from the remaining chromosomes ChromosomeNames ChromosomeNames R A 1IV V VI VII V Pall G XI XII X X1IV XV XVI mito 21 You can set your genome to be able to find genes with the same names in other genomes There are two ways to set up the genomedef file as shown below For details on this feature please refer to Making Lists of Homologs and Orthologs on page 4 31 AcceptedDirectTranslation genomel genome2 Or AcceptedDirectTranslation genomel1 AcceptedDirectTranslation genome2 Make sure you save the genomedef file after you create it Appendix l 7 Copyright 1998 20
148. am eter defined as a replicate is graphically a hidden variable Defining a parameter as a replicate is the easiest way to deal with repeated samples inside GeneSpring The equation used for averaging repeated samples is exactly the same one used to average repeated measurements in a raw data file See Dealing with Repeated Measurements on page G 16 for more information The only difference is the averaging done to repeated parameters is done after the raw data has been normalized Continuous Element A continuous variable is one where each value of the experimental parameter exists in series on a continuum with the other values in that experimental parameter rather than as discrete points Each parameter value is related to the parameter values on either side of it and adjacent data points are connected together by lines Typically continuous variables are numeric This requires the parameter values be in a particular order GeneSpring will automatically order numerical parameters from highest to lowest and order non numerical parameters in alphabetical order When graphing by a continuous parameter each parameter value is placed on the X axis in order from left to right You can change this default order please refer to Re order the Parameters on page 2 10 for more details Non Continuous Element Set A non continuous or set variable is when each parameter value of the experimental parameter exists independent of each other
149. ame and select Duplicate Experiment from the resulting pop up menu The Duplicate Experiment dialog box will appear 2 Name your experiment or accept the default 3 Click OK Your new experiment will appear in the Experiments folder in the navigator Loading from Subchips Sometimes due to oddities in the way region normalizations are done you will need to enter each chip as a separate experiment and merge them together Creating a Genome through the Autoloader In GeneSpring a genome includes all the genes on your chip When you create a genome through the Autoloader GeneSpring creates a genome on the fly based on genes in your experiment data files This means that unlike a genome created in the New Genome Installation Wizard a genome created through the Autoloader has no annotations and no means of obtaining annotations from public databases The genome consists of a master table of genes and a genome definition file If you create a genome through the Autoloader after accepting a file format recognized by Gene Spring anything not standard to that recognized format will not be included in the master table of genes The master table of genes contains all the information associated with genes in a given genome For example if GeneSpring recognizes an Affymetrix file but that file has GenBank accession numbers the numbers will not be loaded You can add these numbers later to column 10 of the master table of genes If your data f
150. ame column then double click the correct file name in the Files present box If the files names are not present in the box please double check to make sure your files are saved in the correct folder within GeneSpring e My samples are in multiple files with different format If your samples are in various files that do not have exactly the same format select My samples are in multiple files with different format You will not be able to continue until every field is filled and GeneSpring has veri fied the existence of each and every file You might need to put more than one file in a field To do this e Place one file in the field in the normal fashion e Manually type in a semi colon after the file name e Hold down the control key Ctrl while selecting the file you would like added to that same field You can do this with either the My samples are in multiple files that share a common format optionortheMy samples are in multiple files with different format option b Select the Next button to continue 10 The Data File Header Lines panel will appear The first drop down menu in this panel allows you to tell GeneSpring whether there are any column titles in your experimental data files If you do a Selecthas a line of column titles after If you have any comment lines to discard type the number of comment lines to be skipped the box GeneSpring automatically skips blank lines so you should not count blank lines among the
151. an 10 of the gene names match your current genome you will get a warning box d Select the Next button to continue The Gene Name Prefix Removal panel will appear This panel allows you to remove one of two types of prefixes from the gene names in the experimental data file so the gene names match the gene names given in the list of genes defining the genome If your genes do not have prefixes it is acceptable to leave the answers to both questions No a Ifevery gene has the same string of characters prepended to it select the Yes circle for the first question Does the name appearing in the gene name column have a fixed unchanging prefix you want removed b Enter the string of characters prepended to your gene names in the Enter fixed prefix box that appears Or a Ifevery prefix is not the same for every gene it prepends but it always ends with the same character If this is the case select the Yes circle of the second question Does the name appearing in the gene name column have a prefix ending in a particular character or charac ters b Enter the character marking the end of the prefix in the box labeled Enter prefix marker character s There may be multiple different markers indicating the end of the prefix If this Appendix D 9 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard 14 15 is the case enter them all in the Enter prefix marker character s box Do not separ
152. ance New York John Wiley amp Sons Inc Appendix N 4 Copyright 1998 2001 Silicon Genetics Technical Details for the Predictor Gene Selection Appendix O Technical Details for the Predictor Gene Selection In order to select genes for use in the predictor all genes are examined individually and ranked on their power to discriminate each class from all others using the information on that gene alone For each gene and each class all possible cutoff points on gene expression level for that gene are considered to predict class membership either above or below that cutoff Genes are scored on the basis of the best prediction point for that class The score function is the negative natural loga rithm of the p value for a hypergeometric test Fisher s exact test of predicted versus actual class membership for this class versus all others A combined list containing the most discriminating genes for each class is produced as the predic tor list Each class is examined in turn and the gene with the highest score for that class is added to the list if it is not already on the list Then genes with the next highest scores for each class are added This is continued in rotation among the classes until the specified number of predictor genes is obtained If you save the list of predictor genes as a Gene List the best prediction score of the gene among the classes for which it would have been added to the list is saved as the attached number on t
153. and is disabled The Options Submenu The Options submenu presented at the bottom of the right click pop up menu in the genome browser It contains a number of possible options Not all of these will be present as many are dependent on the type of view selected Most are simple toggle switches simply select the same command again to turn it off Mac Users should use Control Click to activate pop up menus e Change Vertical Axis Range You can use this command to change the upper and lower bonds of the vertical axis range By using this command you can widen or compress the amount of information seen in the genome browser Select Change Vertical Axis Range and the Parameter Bounds box will appear Type in the new values and click OK For more details please refer to To view a Scatter Plot on page 3 16 e Load Sequence If you see this command it is time to update your version of GeneSpring as versions 4 0 and later load the sequence information automatically Please refer to Update GeneSpring on page A 2 for details If you have an older version you can explicitly load sequences by right clicking while the cursor is in the genome browser A menu will appear Go to the Options menu and select the Load Sequence option A window saying Please wait while nucleic acid sequence is loaded will appear After the loading is complete it is possible to zoom in and see the nucleic acid sequence of a particular gene Loading the sequen
154. ant to waste time re calculating the predictor list each time The minimal experiment will be saved in your Experi ments folder The Save Predictor Genes button saves a list of your predictor genes Genes are ordered according to their predictive values The gene list will be saved in your Gene Lists folder Interpreting the Results of a Prediction The Prediction Results window will appear after you have made a prediction or validated a train ing set For convenience not all of the prediction statistics are visible until you click the Show Details button at the bottom of the window True Value the true value of the class of each sample as calculated when the parameter for the test set is already known Compare this with the value in the Prediction column to validate your training set Prediction the predicted class P value ratio the P value ratio or the probability that the prediction was made by chance for the two classes If you have more than two classes the ratio is the lowest P value divided by the next lowest P value Class counts the individual class counts for each sample P value probability that individual class counts were found by chance The Class Predictor is designed for experiments with at least 20 or so samples in each class It is possible to use the Predictor when you have very small sample sizes if you disable the P value cutoff function For sample sizes of less than 5 please specify 1 or 2 number of
155. ape or portrait 6 Click OK A Save As window will appear Choose a directory type in a file name and click Save Note that you may need to save your file as a large custom size such as 150x150 inches to ensure all your data is included in the saved image Note also that your image will be saved as a vector image which is expandable and that data that is too small to see in the genome browser will be saved in most cases and will reappear when you expand the image Be aware that images contain ing a very large number of genes can require an exceptional amount of memory The fewer genes included in an image the smaller the image file and consequently the easier the image will be to open and manipulate in another program Copyright 1998 2001 Silicon Genetics 6 1 Exporting GeneSpring Data Saving Pictures and Printing To save the Colorbar or Venn Diagram 1 Display the colorbar or Venn diagram you wish to save in the display window 2 SelectFile gt Save Image and choose Colorbar or Venn Diagram A Save As window will appear 3 Choose a directory and file name and click Save To save the Entire GeneSpring window e Windows PC Press the Alt and Print Screen keys simultaneously to copy a picture of the current active window Paste the image into any program that accepts graphics and save it e Macintosh Press Shift 4 Caps Lock simultaneously The cursor will change to a bull s eye Click on a GeneSpring window to save t
156. ar In this panel you can define the parameters as being numbers plotted on a log scale and the units associated with them a Use the scroll bars to view each parameter selecting by leaving a checkmark in the box or leaving blank items for each of the parameters set up in the previous panel You will need to type or paste in the units in the units box at the end of the row It is perfectly acceptable to leave all the options unselected b Select the Next button to continue 7 The How to Display the Parameters panel will appear In this panel you tell GeneSpring what parameter types to use in the default interpretation There are four possible choices The default setting is Denotes a non continuous variable separating the data into discrete graphs viewed side by side on the screen the non continuous display For more detailed information about all of these parameter displays see Parameter Display Options on page 2 12 a Select a new option or leave the defaults for every parameter b Select the Next button to continue 8 The Parameter Values panel will appear In this panel you tell GeneSpring the parameter val ues for each condition in the experiment Initially blank this screen has been filled in with the Parameter Values A parameter value is one of the possible values a variable can have For a more detailed explanation of parameters and how they can be used please see Definitions of Parameters on page 2 11 I
157. arate files or add columns to the file you already have For example a data file containing the signal intensities from sample 1 and sample 2 must have these results in two different columns When this is done the control strength column in the data file pertaining to sample is not in the same place as the column containing the control strength for sample 2 This means the experimental data file layout for sample 1 is not the same as the layout in sample 2 An experiment reported in this way with some but not all of the samples in the experiment reported in the same data files cannot be considered to have the same data file layout To tell GeneSpring your data is reported in this manner answer No to the first two questions in the Describe your Data panel the Are all of your samples in the same data file question and the Do all the data files have the same layout question Enter the name of the experimental data files containing each sample in the File Name column of the table Now the table allows you to repeat a file name in multiple rows unlike the non repetition if you answer Yes to the Do all the data files have the same layout question However if you must use the same data files the same number of times for example sample 1 4 could be named a txt sample 5 8 could be b t xt and 10 12 could be c t xt To con tinue the same example sample 1 4 could be a txt sample 5 6 could not be b txt sample 7 8 could not be c t xt and sample 10 1
158. as Normalizing to the distribution of all genes e Normalizing to a Constant Value hard number e Normalize Each Gene to Itself also referred to as Normalizing to the median for each gene e Normalize all Samples to Specific Sample e Region Normalization You can follow the directions in any or all of these sections as appropriate to normalize your data In a few cases it would not make sense to apply two options together for instance normal izing each sample both to a positive control and across the whole sample or normalizing each gene to itself across all samples and to a specific sample The GeneSpring Experiment Wizard will only allow you to choose one of each of these Other than those instances you may choose any options appropriate to your data The order the normalizations are performed in is mathemat ically significant GeneSpring performs normalizations in the order listed above Three normal izations can be applied either to samples or regions normalize to negative controls normalize to positive controls and normalize each sample or region to itself and are assumed to apply to sam ples unless otherwise specified See Region Normalization on page G 15 for further informa tion For instructions on how to implement any of these normalizations from within GeneSpring see Experiment Normalizations on page 2 21 There is one normalization in addition to those listed whose implementation is automatic repeated
159. ate multiple markers in any way anything you use to separate the characters including a space will be considered a prefix marker character and be removed from the gene name along with anything preceding it Make sure when you are entering a set prefix or a prefix marker character you get the spelling and capitalization exactly correct c Click the Next button to proceed to the next panel The Gene Name Suffix Removal panel will appear This panel allows you to remove suffixes from the gene names in the experimental data file to make the gene names given there match the gene names given in the list of genes defining the genome If your gene names do not have suffixes it is acceptable to leave the answers to both questions No If your gene names have suffixes to remove the suffixes can be one of two types a The first is a set suffix this means every gene with a suffix has the same string of char acters appended to it Click the Yes circle under the question Does the name appearing in the gene name column have a fixed unchanging suffix you want removed b In the box that appears labeled Enter suffix marker character s enter the characters of the suffix Or a The other type of suffix is not the same for every gene name it appends to but it always starts with the same character If this is the case select the Yes circle of the second question Does the name appearing in the gene name column have a suffix that begins in a
160. ate with your sample select the Yes circle The panel will expand If you have a picture already in the correct directory to associate with every sample GeneSpring will display the file name s in the lower right hand corner of the main window In the table labeled G F File Name enter the complete file name of the picture asso ciated with the sample by double clicking one of the file names or typing in each file name Appendix D 11 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard 19 20 manually The picture must be a gifor a jpeg file If one of your samples does not have a pic ture associated with it leave its field blank GeneSpring will use the picture associated with the next closest sample The easiest way to fill in this table is to have all of your gif or jpeg files in the experiment directory Then the file names will appear in the white box at the bottom of the panel Just double click on each picture in the correct order When you right click the GIF File Name table in this panel of the Experiment Wizard there are pop up menus allowing you to cut and paste If you right click one of the gray areas of this table a pop up menu will appear from which you can select copy and paste options You can still cut and paste entries into the matrix fields by using the keyboard commands for Windows this is Ctrl C and Ctrl V a Select the Next button to continue The Array Photos panel wi
161. ats laure tat a R E Talat gaa tah coats 4 24 Adding a Gene to a Pathway wiicdsh lt asa2hustesaceatansunrians adduct bstorer sdaund teptseiatiareesdadsbaiones 4 24 Adding KEGG Pathways civsiessscsasts deassestecersdistetylacdetasarneangedaiecerbdeaedosecnuten 4 25 Finding New Genes on a Pathway scsi secsscdnts laden sikticlasntdeitiesdeviasacl cal sib neate 4 25 R g lat ry SEQUENCES ci Nee Money aust Sasa e suet a n ie EA a R i a 4 26 Making Lists of Homologs and Orthologs ss sssesssessesseesseesssseesseesseseesseessesressesse 4 31 SCRPIS matea ee e e a a a a 4 32 ASIA SOLIDS 12a eranan i ane u a R a a alae 4 32 What isa Script 2 512 Tie ar a a E seeded awe 4 32 Creating Your OWNSCrPtS mennan i a ar iiia 4 34 A to P blish TOE NGL orang a ea o ea ai aa i a en 4 40 3 Copyright 2000 2001 Silicon Genetics Extemial Programs cates Aaaven terete ana Aeacans Gusta Lasecna a ages Ree idee 4 40 GeneSpring External Program Interface cc ceeccceeseeesceesseceeceeeeeeeesseenteenaes 4 40 Examples css reir aera een evo cans nea a e Pade eae auales 4 42 Chapter 5 Clustering and Characterizing Data in GeneSpring cccscsscsseesees 5 1 RTS 2 ce Na aN et a a at Ne a ees oa ei ete tr 5 1 Creating a New Gene Trebonian ro aa laird E nara A AE 5 1 Creating Complex Experiment Trees ssssssssessesssesresseesresresseeseesresseeseesesressee 5 2 References for Hierarchical Clustering casinos davai ee aes is 5 4 Principal Componen
162. ault display will place the selected gene list on both axes 3 Ifdesired select a second gene list from the navigator by right clicking on a gene list and selecting the Display as second list option To remove this second list select the View gt Remove Secondary Gene List Copyright 1998 2001 Silicon Genetics 3 25 Viewing Data in GeneSpring Graph by Genes View Graph by Genes View The Graph by Genes view allows you to visualize an experiment as one line where each point on the line represents the relative expression of one gene S GeneSpring 4 1 Yeast Genes like Y MR199W CLN1 0 95 z ioj x File Edit Views Experiments Colorbar Tools Annotations Window Help Eere Relative Yeast cell cycle time series no 90 min Gene Ontalo PIR keyworc 4 all genes all genomic 8 ACGCGT in Mike YMR199 BS Experiments Random Da Gene Trees eH Experiment Tree Classifications Pathways Array Layouts Drawn Genes EH External Progra Bookmarks Scripts Gene time 20 0 minutes Animate Magnification 1 zoom Out m EE Figure 3 13 The Graph by Genes view limited to the Like CLN1 list Figure 3 13 shows the genes in the like YMR199W CLN1 0 95 list in Graph by Genes view Genes at the top of the selected gene list are displayed at the left end of the experiment line and genes at the bottom of the gene list are displayed at the right end of the experiment line Gener
163. ayout file for a particular experiment rather than explaining exactly what each possible answer means There are two examples following each question The first is the generalized form of the answer including the generalized object name and what sort of response constitutes a correct object value The second bold faced example is an example of an actual answer to the question A complete layout file for the fictitious Yeast extraterrestrial studies experiment is given at the end of this chapter The four possible lines in the layout file are 1 Include this line if your experiment has positive controls This line refers to a file listing the positive control If you have positive controls you must have a separate file designating them See The Positive and Negative Control Files on page 7 for information about this file PosControlFilename the complete file name of the file listing the gene names of the positive con trols one per line PosControlFilename PosControls txt 2 Include this line if your experiment has positive controls This line tells GeneSpring if you want to display the positive control genes in the genome browser with the rest of the experi ment as if they were genes from the organism you are studying Type true as the object value for this line if you wish to view the positive controls in the genome browser and enter false if you do not IncludePosControls true or false IncludeP
164. bases automatically keep records organized and enable you to search for or pull out particular records based on any field in the record The software allowing you to create and main tain databases is called a Database Management System or DBMS In database terminology a file is called a table Each record in the file is called a row and each field is called a column A relational database is the most common type of database in client server systems Simply stated in this type of database relationships are established between tables based on common information Open Database Connectivity Open Database Connectivity ODBC is an Application Programming Interface API allowing a programmer to abstract a program from a database When writing code to interact with a database you usually have to add code that talks to a particular database using a proprietary language If you want your program to talk to Access Fox and Oracle databases you have to code your pro gram with three different database languages This can be a very difficult or time consuming task This is where ODBC enters the picture When programming to interact with ODBC you only need to speak the ODBC language a combination of ODBC API function calls and the SQL language The ODBC Manager will figure out how to contend with the type of database you are targeting Regardless of the database type you are using all of your calls will be to the ODBC API All you need to do is install
165. below shows the 2 most significant patterns in the 112 genes you were looking at GeneLists have been made for you in the folder PCA Rat Study in the GeneLists folder These contain numbers specifying how much of each component is present for each gene The two most significant have been preset as the axes for the genome browser to change this right click on the gene list and choose the scatter plot menu item corresponding to the axis you desire 1 004Normalized Rat Study colored by Principal components r i n 1 Most c Significal i p a 2 Least E Significal 0 m p o n e Unclassi Embryonic Postnatal Adult Figure 5 1 Principal Components Analysis window When the analysis finishes the Principal Components Analysis window appears displaying each component as a line in graph mode The significance of each component is represented by the color of its graph line as defined by the colorbar Double clicking any of the components will bring up the Gene Inspector window which shows the eigenvalue and explained variability in the upper left panel In addition a new gene list folder will appear in the navigator panel with a name that includes the name of experiment that you used for PCA analysis e g PCA yeast cell cycle Interpreting your PCA Results The principal components of a data set are the eigenvectors obtained from an eigenvector eigen value decomposition of the covariance matrix of the data The eigenvalue c
166. both intended to address the same issue If you do not have either positive controls or a reference it is strongly suggested you normalize each sample to itself This option is also referred to as Distribution of All Genes or Global Scaling Please refer to Nor malizing to the Distribution of All Genes on page 2 23 and Negative Control Strengths on page G 18 Mathematical Illustration of the Normalize Each Sample to Itself Method Given raw data without positive controls or control channel Raw Experimental Results Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1000 2000 1500 CLN2 1000 2000 500 CDC28 100 200 50 HSL1 1000 2000 500 YGP1 10 000 20 000 5000 Appendix G 6 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalizing Each Sample to a Hard Number The results of normalizing each sample to itself After Normalizing Each Sample to Itself Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1 1 3 CLN2 1 1 1 CDC28 0 1 0 1 0 1 HSL1 1 1 1 YGP1 10 10 10 See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring Normalizing Each Sample to a Hard Number You would normally only use this function if you have pre normalized data such as data prepared with Affymetrix s Global Scaling In that instance you would want to divide all data by 2500 or whatever number you chose
167. bs for Correlation type Maximum iterations amp Discard bad Output is a Classification Self Organizing Map Makes a SOM 1 Gene List input amp 1 Experiment interpretation input Knobs for Iterations Discard bad Rows Columns amp Radius Output is a Classifi cation 4 Filtering Filter Fold Change Determines fold change for each gene between 2 conditions and generates a gene list with associated numbers of the genes that have a large enough fold change to pass the filter 2 Condition inputs Knob for Fold change Output is a Gene List Filter Genes with Associated Numbers Takes a gene list and produces a gene list con taining the genes whose associated value is above the specified parameter Gene List input Knobs for Cutoff amp Comparison Output is a Gene List with associated numbers Filter On Condition Produces a gene list containing the genes that have a measurement relative to a cutoff 1 Condition input Knobs for Filter type Filter cutoff amp Comparison Output is a Gene list Filter on Gene Correlation Find the genes that have a certain correlation in an experi ment Find Similar Genes 1 Gene input amp 1 Experiment interpretation input Knobs for Correlation type Cutoff amp Comparison Output is a Gene List with associated numbers Filter on Text in Description Find genes containing the specified text 1 Gene list input Knob for Search term Output is a Gene List Copyright 1998 2001 Silicon
168. ce also allows you to take advantage of GeneSpring s sequence based features such as Find Regulatory Sequences Appendix P 5 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Genome Browser e Show ORF direction Ignore ORF direction A gene is represented visually by a colored line or upon higher magnification a colored rectangle The rectangle s position relative to the chromosome line determines the direction of the ORF A gene below the chromosome line has a reading direction opposite to the direction chosen by the sequencers and the sequence is read backwards You can choose to display this distinction between which direction a gene is read Show ORF direction or to have no distinction between genes Ignore ORF direction Select the Ignore ORF direction command or the Show ORF direction command e Show Complementary Bases just Show One Strand Of Bases Show Complementary Bases allows both the Watson strand 5 and the Crick strand 3 to be shown while viewing the nucleic acid sequence in the physical position display and conversely Just Show One Strand Of Bases shuts this feature off and only displays the Watson strand of the sequence Select the Just Show One Strand Of Bases command or the Show Complementary Bases command e Show Horizontal Label Hide Horizontal Label The horizontal axis is the experiment parameter This command allows the label associated with the horizontal axis to be seen or hidde
169. ceed to the next panel until you have entered a column name or number for the raw data col umn for every sample row and for the background column if present Appendix D 10 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard d Select the Next button to continue 16 The Control Channel Values panel will appear If you have control channel values for each gene on your array then you can use this information to normalize your genes See Normaliz ing Options on page G 1 for more information regarding how this normalization works If you do not have a control for each gene if you did a single color experiment this is proba bly the case you should leave the No circle selected and proceed to the next panel If you do have control channel values select the Yes circle and enter the name s of the col umn or its number containing the control channel signals in the Control Channel Column box If your experiment took a reading of the background for the control channel values change the selection in the bottom question to Yes Then enter the column name s or number s of the column containing the control channel background signal When you enter column names make sure you use the correct spelling and capitalization a Select the Next button to continue 17 The Flags panel will appear If your experimental data contains a column indicating whether the experiment worked for each gene GeneSpri
170. cept the new color Copyright 1998 2001 Silicon Genetics Appendix B 3 Preferences Window Gene Labels Gene Labels This function allows you to specify how you would like to name your genes in the genome browser The defaults are systematic name and common name This feature is particularly useful in the Scatter plot 3 GeneSpring Preferences iof x Gene Labels 7 Primary Gene Label Systematic Name 7 Secondary Gene Label Common Name 7 OK Cancel Help Figure 4 3 Gene Labels details in the Preferences window Browser Details In this box you can set the defaults for your web browser in case you want to use a particular browser for the GeneSpring applications You will only need the use the Browser assignment field if you are using an obscure web browser that requires and argument The Firewall Details box If your company has a firewall to prevent unauthorized use of the internet you will need to use this box to get through it You may need to contact your System Administrator for details about your firewall Appendix B 4 Copyright 1998 2001 Preferences Window The System Preferences The System Preferences The System panel allows you to specify a number of different parameters about networking and memory usage e The License Manager field allows you to specify the IP address of the machine that dis penses concurrent licenses e The GeNet Address field contains the URL of GeNet in your c
171. ces a mark in the list where the relative abundance of the class on one side of the mark is the highest in comparison to the other side of the mark The genes that are most accurately segregated by these markers are considered to be the most predictive A list of the most predictive genes is made for each class and an equal number of genes are taken from each list To make a prediction the class predictor uses the k nearest neighbor method It selects k num ber of samples near as measured in Euclidean distance the unclassified sample and for each class computes a P value that is the likelihood of finding the observed number of this class within the neighborhood members by chance given the proportion of the classes in the training set The class with the lowest P value is assigned to the unclassified sample You can specify a P value cutoff or threshold such that if there is not sufficient evidence in favor of a particular class no prediction will be made The P value cutoff is a ratio of the probability that the prediction was made by chance for the two classes If you have more than two classes the ratio is the lowest P value divided by the next lowest P value To use the Class Predictor 1 Select Tools gt Predict Parameter Values The Predict Parameter Values window will appear 2 Open the Experiments folder in the mini navigator and click your training set the set of sam ples for which the parameters are already known Click
172. ch lessens the value of the list Multiple testing corrections adjust the individual p value to account for this effect Suppose the p value cutoff is a and the number of genes being tested is N The first three proce dures Bonferroni Holm and Westfall and Young control the family wise error rate FWER which is the overall probability of obtaining even a single false positive test to be no more than a This is a very strong criterion but may be so strong for large lists of genes that no genes are iden tified as significant The Benjamini and Hochberg test controls the false discovery rate defined as the proportion of genes expected to be identified by chance relative to the total number of genes called significant Copyright 1998 2001 Silicon Genetics 4 5 Analyzing Data in GeneSpring Filter Genes Analysis Tools Bonferroni The Bonferroni multiple testing correction based on Bonferroni s inequality limits the chance of a false positive results to be no more than a by multiplying each nominal p value by N with a maximum of 1 This process controls the FWER and the expected num ber of genes by chance is a Bonferroni step down Holm The Holm step down adjustment computes the most signifi cant p value and whether it meets the a cutoff after multiplying by N If that gene is found to be significant then the next most significant gene is considered but the gene that was found significant is removed from the multiple testing so th
173. change the color defaults to any of the listed colors until you find a combination you like and is easy for you to see on the screen GeneSpring Preferences OF x Color 7 Upregulated color change Normal color fro colors change VILEEELL LE Downrequlated color change Structure color fn change Background colo change Selected 2A change OK Cancel Help Figure 4 1 The Colors section of the Preferences window e Upregulated Color The Upregulated Color is the color that will be used to display genes greater than or equal to the High Expression value selected for the current color bar The default for this color is red The brightness of the color depends on the trust associated with it Please refer to Trust on page 3 32 e Normal Color The Normal Color is the color used to represent genes having a normalized expression value of one The default for this color is yellow e Downregulated Color The Downregulated Color is used to display genes less than or equal to the Low Expression value selected for the color bar The default for this color is blue Over and under expression color refers to the coloring of genes as shown in the genome browser and color bar You can change the definitions of overexpressed upregulated and underexpressed downregulated genes by right clicking over the colorbar in the main genome browser and reset ting the defaults Please refer to Cha
174. chip normalizations click Use positive control genes 3 Browse to find your positive control file 4 Enter a cutoff in the Use Values Over box telling GeneSpring not to do the normaliza tion if the median of your chip is below this cutoff Copyright 1998 2001 Silicon Genetics 2 22 Creating DataObjects in GeneSpring Per chip Normalizations e One caveat regarding normalizing to positive controls This normalization will not control for variations in the total harvest of mRNA across samples If you are concerned about this variation you may want to instead normalize to the distribution of all genes Normalizing to the Distribution of All Genes The most common way to control for systematic variation is by normalizing to the distribution of all genes The formula for this is signal strength of gene A in sample X specified percentile of all of the measurements taken in sample X To Use Distribution of All Genes 1 Under Per chip normaiizations in the Experiment Normalizations window click Use dis tribution of all genes 2 Typically you will use the default percentile 50th 3 Enteracutoffinthe Use Values Over box telling GeneSpring not to do the normalization if the median of your chip is below this cutoff e One caveat This sort of normalization assumes that the median signal of the genes on the chip stays relatively constant throughout the experiment If the total number of expressed genes in the experiment changes dramat
175. column con taining the raw data you may use the general object name given below rather than entering the column number of the raw data for each file IntensityColumn number of the column containing the signal intensity data IntensityColumn 7 18 If your data file has a column indicating the background signal tell GeneSpring which column contains that information If your data does not have a background reading skip this question and the associated experiment file entry Experiment IntensityBackColumn number of the column containing the background reading for the sample indicated ExperimentlIntensityBackColumn 5 Experiment2IntensityBackColumn 10 Experiment3IntensityBackColumn 15 Experiment4IntensityBackColumn 20 Experiment5IntensityBackColumn 25 Experiment 6IntensityBackColumn 30 Experiment 7IntensityBackColumn 35 If your data is all in the same file you will have to indicate the background reading column for each sample illustrated above This is also true if you have two or more data files with different columns containing the background data If on the other hand you have separate data files with the same column containing the background data you may use the general object name given below rather than entering the column number of the background data for each file IntensityBackColumn number of the column con taining the background reading IntensityBackColumn 8 Appendix J 1
176. come up with these motifs is given in the last column This is the number of oligomers tested that were the length of the sequence motif found Copyright 1998 2001 Silicon Genetics 4 28 Analyzing Data in GeneSpring Using the Conjectured Regulatory Sequence window The Conjectured Regulatory Sequence window displays the common nucleotide sequence show ing the 10 bases that precede and follow it in the area near or in each gene where the oligomer is found It also gives a brief description of the statistics listed in the Results box of the Find Poten tial Regulatory Sequences window and allows you to modify the observed motif by removing an item extending the promoter or making a new gene list Double clicking one of the sequence motifs given in the Results box of the Find Potential Regula Regulatory Sequences tory Sequences window will bring up the Conjectured Regulatory Sequence window gt Conjectured Regulatory Sequence els File List Details The sequence AAACGC was observed upstream of 35 out of the 117 genes in the gene list called like YMR199VV CLN1 0 95 Upstream means from 10 to 500 bases upstream of the gene Only exact matches were counted This was compared to the frequency 11 937 of that sequence upstream of other ORFs in the genome Yeast Ifthe distribution of bases were random you would expect to see that sequence upstream of 11 168 ofthe genes The probability that this particular
177. computer C WINNT java exe cp D Program Files SiliconGenetics Gene Spring bin GeneSpring jar GeneSpringMain to 1 2 Copyright 1998 2001 Silicon Genetics Introduction C WINNT java exe mxl64m cp D Program Files SiliconGenet ics GeneSpring bin GeneSpring jar GeneSpringMain If you are still experiencing slowdowns check the memory usage by selecting Help gt System Monitor before invoking any functions Make a record of the Total Memory and Free Memory listed in the System Monitor window and contact Silicon Genetics Technical Services Depart ment at 650 SIG SOFT or support sigenetics com Updating GeneSpring If you already have GeneSpring and just need to obtain the latest update select Help gt Update and follow the on screen instructions to obtain the current GeneSpring jar Learning to Use GeneSpring Silicon Genetics provides a variety of ways to improve your knowledge of GeneSpring In addi tion to this manual there is online help Flash tutorials a PDF tutorial and face to face work shops that cater to beginning intermediate or advanced users Where to find help Workshops http www sigenetics com cgi SiG cgi Support workshops smf Flash tutorials http www sigenetics com cgi SiG cgi Demos tut_ welcome smf Tech notes http www sigenetics com cgi SiG cgi Documentation GSTN smf FAQs http www sigenetics com cgi SiG cgi Documentation GSFAQ smf GeneSpring
178. ctAnnotations gt Make Gene Lists from Properties 2 Choose the property you would like to use for generating lists and click OK To make a list based on biological function 1 SelectAnnotations gt Build Simplified Ontology 2 Name your new list and click OK To make lists from a group of selected genes 1 While the group of genes is still highlighted right click over the highlighted area and select Make List from Selected Genes from the pop up menu You will find your new lists in the Gene Lists folder Copyright 1998 2001 Silicon Genetics 1 12 Introduction GeneSpring Basics Tips for Mac Users Except where otherwise noted instructions in this manual describe GeneSpring usage on a PC If you are a Mac user you will find the following keystroke and mouse conversion information helpful e Right Click Hold the Control button and click This will most often activate a pop up menu e Ctrl 3 Wherever the manual mentions Ctrl for example press Ctrl I to reach the Gene Inspector substitute the Apple key for Ctrl e Drawing genes on a pathway Hold down the Option key and drag your cursor diagonally to draw a gene on a pathway See Pathways on page 4 23 for more information Note that on a Macintosh computer the menu bar is at the top of the screen not on the individual GeneSpring windows as displayed in this manual The Navigator GeneSpring organizes data elements relating to your genome into folders in the
179. cts Copyright 1998 2001 Silicon Genetics 6 6 Exporting GeneSpring Data Publish to GeNet GeNet can also generate reports including experiment reports e gene list reports e annotated data Every folder genome list tree etc that can be uploaded to GeNet will have aPublish to GeNet menu item in its right click pop up menu Once selected the GeNet Upload window will appear Type in any necessary information Once you click the Upload button you will see a new dialog box This box will contain information on the progress of the upload Each item if you are uploading an entire folder will have its own line If GeNet is not available or if you are unable to load data for another reason you will get an error message If you specify a nonexistent destination directory GeNet will create one If you are having trouble uploading ask your administrator to check and make sure your default directory exists It can easily be added if it does not exist Depending on the initial set up of GeNet you may not have access to every directory Once your upload is complete the upload status box will say it is complete Click the Close but ton or the small x in the upper right corner Uploading Genomes to GeNet You must have administrator access privileges to upload genomes to GeNet If you cannot upload genomes and feel you should please contact your system administrator To upload a genome to GeNet go to File gt Publish Genome to GeNet
180. d in the title bar also from experiment 2 e bis the weight associated with experiment 2 e Cis the correlation coefficient of the gene in question in experiment 3 to the gene named in the title bar also from experiment 3 e cis the weight associated with experiment 3 and so on Experiments 1 2 3 and so forth are all of the experiments selected in the white Correlations box If X is between the minimum and maximum correlations specified in the Multi Experiment Cor relation window then the gene in question passes the correlations e Standard Correlation Standard correlation measures the angular separation of expression vectors for Genes A and B around zero Result a b a b e Smooth Correlation Make a new vector A from a by interpolating the average of each consecutive pair of elements of a Insert his new value between the old values Do this for each pair of elements that would be connected by a line in the graph screen Do the same to make a vector B from b Result A B A B e Change Correlation Make a new vector A from a by looking at the change between each pair of elements of a Do this for each pair of elements that would be connected by a line in the graph screen The value created between two values a and a is atan a a 7 4 Do the same to make a vector B from b Result A B A B e Upregulated Correlation Make a new vector A from a by looking at the change between each pair of elements of a
181. d how to apply them please refer to Filter Genes Analysis Tools on page 4 1 e To remove genes that may skew the clustering results due to missing measurements click the Discard Genes With No Data for Half The Conditions box 3 To add an experiment or condition click on the experiment or condition in the Experiments folder in the mini navigator click the Add button and enter a weight in the New Experiment dialog box The weight of a condition or experiment is a measure of the influence it has on the correlation distance e g an experiment with a weight of 2 0 will be twice as influential as one with a weight of 1 0 To remove an experiment or condition click on the experiment or condi tion under Experiments to Use and select Remove 4 Choose the number of rows and columns in your grid The default settings for the fields described in steps 5 6 and 7 are based on the number of genes and conditions in your exper iment To return to the default settings after having changed these values click the Default Values box at the bottom of the Clustering window A good way to estimate the optimum number of rows and columns is to try to predict how many distinct classes of genes are affected by the conditions in your experiment With small data sets the algorithm may gener ate a number of empty nodes To avoid this you might try using a smaller grid Copyright 1998 2001 Silicon Genetics 5 12 Clustering and Characterizing Data in GeneSpri
182. d indicate what type of assumptions you would like GeneSpring to make about the precision of these averaged values You can display and perform analyses on the normalized data using three modes ratio raw versus control strength logarithm of ratio or in terms of fold change versus the control strength t is important to note that the graphical display of normalized values and the numbers used for all analyses such as clustering reflect the mode you have chosen However the numbers displayed as text as in the Gene Inspector window and entered by the user as parameters for analyses as in the Filter Genes tools are always in ratio mode Copyright 1998 2001 Silicon Genetics 1 8 Introduction GeneSpring Basics Loading Your Data The demonstration version of GeneSpring comes pre loaded with sample yeast rat and human data Many users benefit from performing trial analyses on these sample data sets When you are ready to analyze your own data you will need to load and set up your data for analysis There are four main steps to preparing data 1 Loading gene information optional 2 Loading experiment information 3 Telling GeneSpring how to interpret the information by assigning normalizations parameter values and modes of display 4 Annotating updating your genome To Load Your Data e Step 1 Load gene information from your arrays optional a Start GeneSpring and select File gt New Genome Installation Wizard b Type
183. d normality usually 1 is actually less than indicated Normalized Control and Raw data are also displayed in the upper right corner of the Gene Inspector window Data File Restriction The Data File Restriction allows you to filter genes based on values in a specific column of your experiment data files For example if you specified a flag column when you loaded your data you can filter on Present or Marginal calls You can select any column name from your experiment from the Column drop down menu Alter natively you can enter the column number in the Number box If you have access to the original data files entered in GeneSpring you can check them for column numbers You can restrict the column values by choosing greater than equal to or less than from the pull down menu and inserting a restriction value in the field provided For example if you had loaded an Affymetrix file as your experiment you could use the drop down menu to select the Abs call column and select for all entries equal to M if you wanted to make a list of just the marginal data Copyright 1998 2001 Silicon Genetics 4 8 Analyzing Data in GeneSpring Filter Genes Analysis Tools Restricting by Associated Numbers New in version 4 1 is the ability to restrict genes according to the numbers associated with them in a gene list When you make a new list based on a filter or similarity metric the value used as a fil ter will be associated w
184. der is unfamiliar with your file format you can use the Column Editor to specify the type of data in each column Once the Column Editor learns the location and identity of the rele vant columns of data it adds these specifications to its list of known file types so that you can load subsequent experiments in batch Make sure you use the raw tab delimited files just as they come out of the scanner as GeneSpring uses the information in the column headers If you have cut out header information you will need to find your original tab delimited data files and use those To Autoload an Experiment 1 SelectFile gt Autoload Experiment or Ctrl1 o0 2 2 Choose the data file or folder you wish to load Make sure all the files have exactly the same format 3 If GeneSpring correctly identifies your file format click Yes The Select Genome window will appear IfGeneSpring does not correctly identify your file format choose No A dialog box will appear asking you to set up column formats for your data or use the Experiment Import Wizard a Ifall your files are in the same format choose Yes This will bring up the Column Editor See To set up Column Formats on page 2 2 Copyright 1998 2001 Silicon Genetics 2 1 Creating DataObjects in GeneSpring The Experiment Autoloader b If your files are not in the same format choose No This will exit the Autoloader You will need to use the Experiment Import Wizard The Experiment Wiza
185. details On a Windows machine this will be found in C Program Files on a Mac in the Applications folder When the key is about to expire you will get a warning message 30 days in advance If your license has expired or is about to please contact Silicon Genetics at 866 SIG SOFT 744 7638 Setting Memory Usage Options Once GeneSpring is installed you will need to make sure the default memory setting in Gene Spring preferences is half of your computer s available memory or more if you have lots of RAM To do this select Edit gt Preferences choose System from the pull down menu and enter the amount of memory in the Desired Memory Use field Configuring Virtual Memory on your hard drive Generally the minimum recommended amount to have available as virtual memory is 150MB RAM Check to make sure large files are not restricting programs from running as quickly as they might You may be able to move some large files to another drive If you are using the IBM JVM make sure you specify in the path the appropriate amount of mem ory to use You can reach the path by right clicking the GeneSpring icon on your desktop and choosing Properties from the pop up menu The MS JVM and the Macintosh JRE is set to use more of the available memory but the IBM JVM will as a default use 64MB RAM For instance the path specified for the java exe classpath should be changed to include a mem ory amount equal to about half the RAM on your
186. displayed You can change the upper and lower bounds of the vertical axis of your graph the mode used to represent your data whether to turn on the global error model how you would like to view each parameter and which flagged measurements you wish to be displayed Changing an experiment interpretation is useful not only for customizing initial display settings but also because statistical analysis techniques in GeneSpring are carried out based on how your data is characterized in the interpretation Because of this it can be valuable to set up more than one experiment interpretation then perform analyses on each one to compare the results of statis tical testing on data that has been grouped and characterized in different ways When you load your experiment GeneSpring automatically creates a Default Interpretation and an All Samples interpretation The Default Interpretation is the first item listed under the experiment in the navigator You will find it convenient to set up your most frequently used interpretation as your Default Interpretation You can rename the Default Interpretation but you cannot delete it The All Samples interpretation makes all parameters non continuous so that each parameter is viewed and analyzed individually The All Samples interpretation cannot be changed renamed or deleted To change the Experiment Interpretation 1 SelectExperiments gt Change Experiment Interpretation The Change Experiment Interpretati
187. dition McGraw Hill Book Co New York 1976 Pearson K On Lines and Planes of Closest Fit to Systems of Points in Space Philosophical Mag azine 6 2 559 572 1901 Rao C R The Use and Interpretation of Principal Component Analysis in Applied Research Sankhya A 26 329 358 1964 Raychaudhuri S Stuart J M and Altman R B Principal components analysis to summarize microarray experiments application to sporulation time series Pacific Symposium on Biocom puting 2000 Copyright 1998 2001 Silicon Genetics 5 8 Clustering and Characterizing Data in GeneSpring k Means Clustering k Means Clustering k means clustering divides genes into groups based on their expression patterns The goal is to produce groups of genes with a high degree of similarity within each group and a low degree of similarity between groups Unlike self organizing maps k means clustering is not designed to show the relationship between clusters Instead k means clusters are constructed so that the aver age behavior in each group is distinct from any of the other groups For example in a time series experiment you could use k means clustering to identify unique classes of genes that are upregu lated or downregulated in a time dependent manner GeneSpring s k means clustering algorithm divides genes into a user defined number k of equal sized groups based on the order in the selected gene list It then creates centroids in expression space
188. ditions e Filter Experiment Group For each Boolean in the first argument pass through the cor responding Experiment interpretation if the Boolean is true 1 Boolean Group input amp 1 Experiment interpretation Group input Output is a Group of Experiment interpretations e Filter Experiment Tree Group For each Boolean in the first argument pass through the corresponding Experiment Tree if the Boolean is true 1 Boolean Group input amp 1 Experiment Tree Group input Output is a Group of Experiment Trees Copyright 1998 2001 Silicon Genetics 4 38 Analyzing Data in GeneSpring Creating Your own Scripts Filter Gene Group For each Boolean in the first argument pass through the corre sponding Gene if the Boolean is true 1 Boolean Group input amp 1 Gene Group input Out put is a Group of Genes Filter Gene Classification For each Boolean in the first argument pass through the cor responding Classification if the Boolean is true 1 Boolean Group input amp 1 Classifica tion Group input Output is a Group of Classifications Filter Gene List Group For each Boolean in the first argument pass through the corre sponding Gene List if the Boolean is true 1 Boolean Group input amp 1 Gene List Group input Output is a Group of Gene Lists Filter Gene Tree Group For each Boolean in the first argument pass through the corre sponding Gene Tree if the Boolean is true 1 Boolean Group input amp 1 Gene Tree Group input
189. duce the number of genes to be made into a tree To begin an Experiment Tree 1 Select Tools gt Clustering 2 SelectExperiment Tree from the Clustering Method pull down menu 3 Select a gene list from the Gene Lists folder in the Clustering window 4 To add an experiment interpretation or condition click on one of these items in the Experi ments folder of the Clustering window click the Add button in the Experiments to Use sec tion and enter a weight in the pop up window Or Right click an experiment or condition in the Clustering window ad choose Add Experi ment Correlation from the pop up menu Enter a weight in the pop up menu and click OK e You can add multiple experiments interpretations or conditions e You can right click experiment interpretation or condition to add a restriction See Filter Genes Analysis Tools on page 4 1 and Making Lists with the Complex Correlation Command on page 4 14 for details 5 Choose a measure of similarity from the pull down menu See Equations for Correlations and other Similarity Measures on page L 1 for details 6 Choose a separation ratio See Minimum Distance and Separation Ratios on page 5 3 7 Choose a minimum distance See Minimum Distance and Separation Ratios on page 5 3 8 Click Start Note You can right click the list to Add Associated Numbers Restriction if desired See Adding an Associated Number Restriction on page 4 9 Correla
190. ducing artificially downregulated values Appendix G 11 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalizing All Samples to Specific Samples Special cases As an example you might have patients controls and drugs arranged in the following manner There are a total of nine samples Control Patients Drug X 7 1 2 Drug Y 8 3 4 Drug Z 9 5 6 e To normalize the control to itself use this syntax 1 2 7 7 3 4 8 8 5 6 9 9 This will finish with sample 1 divided by raw 7 2 divided by raw 7 and 7 divided by raw 7 All values for the normalized sample 7 will equal one e To normalize the control to the average of controls If you want to see sample 1 divided by the raw 7 sample 2 divided by raw 7 and sample 7 divided by the average of 7 8 and 9 you must use this syntax 1 2 7 3 4 8 5 6 9 7 8 9 7 8 9 This will divide sample 1 by the raw data of 7 sample 2 by the raw data of 7 and sample 7 by the average of sample 7 8 and 9 Mathematical Illustration of the Normalizing Samples to a Specific Sample Method As an example your experiment might be designed with three different types of tissues 3 control samples and 6 treated samples arranged in the following manner There are a total of nine sam ples Control Treated Tissue Type X Sample 7 Sample 1 Sample2 Tissue Type Y Sample 8 Sample 3 Sample 4 Tissue Type Z Sample 9 Sample5 Sample 6 Appendix
191. e Experiment Image the complete file name of the file containing the picture to associate with the indicated file Appendix J 13 Copyright 1998 2001 Silicon Genetics Installing from a Text File Associating a Picture with a Sample If you have a picture associated with every sample this section of your experiment file should look similar to this ExperimentlImage yeastpict1A0 gif Experiment2Image yeastpict1A10 gif Experiment3Image yeastpict1A20 gif Experiment4Image yeastpict1A30 gif Experiment5Image yeastpict1A40 gif Experiment6Image yeastpict1B0 gif Experiment7Image yeastpict1B10 gif If you have only one picture to associate with the entire experiment being described in your experiment file the picture entry should look similar to this one ExperimentlImage happy _yeast_picture gif If you have some pictures to associate with some but not all points in your sample the picture entries in your experiment file should look similar to these ExperimentlImage yeastpict1A gif Experiment6Image yeastpict1B gif ExperimentllImage yeastpictlAndromedaA gif Experimentl6Image yeastpictlAndromedaB gif Experiment21lImage yeastpict2A gif Experiment26Image yeastpict2B gif Experiment3lImage yeastpict2AndromedaA gif Experiment36Image yeastpict2AndromedaB gif Normalizations Negative Controls 24 Do you have any genes designated as negative controls on your array You have negative con trols when there i
192. e at http www grt kyushu u ac jp spad menu html To import a pathway your pathway image must be in a gifor jpeg file format You can manually import the file into GeneSpring by placing it in Program Files SiliconGenetics GeneSpring Data YourGenome Pathways or by doing the following 1 SelectFile gt Open Genome or Array and choose the genome in which you want to place the pathway 2 SelectFile gt New Pathway The Select Image File dialog box will appear 3 Browse for your image file and select it Click Open This will bring up the Choose Pathway Name window 4 Enter a name for your pathway and folder and click Save You can now find your pathway in the Pathways folder in the navigator Adding a Gene to a Pathway Once you have successfully imported your graphics file into GeneSpring you are ready to place genes on top of the background image 1 Open the appropriate Pathway in the navigator 2 While holding down the Ctrl key draw a box where you would like the gene to appear on the pathway Mac users should press Option and drag the mouse The New Genes on Path way window will appear 3 Type in the gene name accession number or keyword such as a word in a gene s descriptor and click OK The gene name should now appear on the pathway To enter multiple genes in one location separate gene names or keywords with semicolons 4 Ifthe gene name or keyword is present for more than one gene another window will ap
193. e Fla Reon in Experiment 1 Strength 8 S 8 CLN1 510 110 10 10 P MEP2 9 19 9 9 M C If created in a spreadsheet program the file should be saved as a tab delineated text file If your computer is set for a non English language that typically uses commas for decimal mark ers GeneSpring will recognize this If for example your computer is set for French the comma will be recognized as a decimal marker You cannot use commas and periods interchangeably GeneSpring can also read experimental data from databases via an ODBC link Please refer to Installing from a Database on page E 1 Pictures of the conditions during the experiment At most there can be one picture associated with each condition You do not need to have any pic tures but they are good mnemonics reminding you of what was happening in the experiment at the point you are viewing in GeneSpring If you have only a few pictures this can be very useful as GeneSpring will use the picture closest to the displayed condition These pictures should be either GIF or JPEG files Pictures of the Microarray plates At most there can be one array picture associated with each sample They are helpful but not nec essary These pictures should be either GIF or JPEG files The Layout file If you load experiments via the Experiment Wizard or the AutoLoader then you will probably never have to create your own layout file and thus you can skip this entire section However if
194. e Help Menu is located on the right of the menu bar GeneSpring Basics Instructional Manual You can download this file from the web and print it if you wish as a PDF document The tuto rial covers many basic topics of GeneSpring Manual Selecting the Manual will launch your browser and take you to C Program Files SiliconGenet ics GeneSpring docs GeneSpringMainScreen html The GeneSpring User Manual is a PDF docu ment you can save or print FAQ Selecting the Frequently Asked Questions will launch your browser and take you to http www sigenetics com GeneSpring faq index html Version Notes Selecting this will launch your browser and takes you to C Program Files SiliconGenetics Gene Spring docs VersionNotes html This page should have all the version notes for your version of GeneSpring Appendix A 1 Copyright 1998 2001 Silicon Genetics Help The Help Menu Update GeneSpring Selecting Update GeneSpring will bring up a window where you can agree to the conditions and get a new version of GeneSpring if your license is still active You can also automatically update the manuals that accompany GeneSpring The manuals are typ ically published at HTML or PDF documents and it is recommended to update them every time you update GeneSpring Selecting this item will launch your browser and take you to a webpage to download a new copy of GeneSpring Make sure it is saved in the correct folder Silicon Genetics on the Web Sel
195. e No circle selected If you are using your control channel values for normalization you need to enter the minimum reference signal to be used in the normalization This is because sometimes the control chan nel value is very low and would artificially inflate the noise for its gene Indicate the minimum value you would be willing to divide a gene s signal by in the Minimum control channel strength box If you are not using your control channel values for normalization then you are using them to indicate the trustworthiness of the experimental data for each gene Indicate the minimum value a reference must have for you to consider the data for the gene it is associated with valid in the box labeled Minimum confidence level For a mathematical illustration of this normalizing option please refer to Normalize to Con trol Channel Values for Each Gene on page G 3 a Select the Next button to continue The Normalizations Positive Controls panel will appear This panel tells GeneSpring if you have any genes designated as positive controls on your array and if you want to normalize your sample using this information You typically have positive controls when there is DNA Appendix D 13 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard from a different genome than the one you are investigating on your array and you add a known quantity of that DNA to your sample If you do not want to normalize
196. e a GenBank file answer Yes to having a GenBank file and enter the file name and pathway of the EMBL file where it asks for the GenBank file name You may need to download a GenBank file please see GenBank or EMBL Files on page H 4 To indicate you have a GenBank or EMBL file a Select Yes If you are not using a GenBank or EMBL file leave the No circle selected and go on to the next panel b Either type the complete file name and pathway of your GenBank EMBL file in the Enter filename box or click the Browse button This brings up the browser window c Look at the folder listing to make sure you are in the folder you want d Click the GenBank or EMBL file for this organism Copyright 1998 2001 Silicon Genetics Appendix C 2 Genome Wizard e Click the Open button This enters the complete pathway and file name of the selected file in the Enter filename box of the Genome Wizard Once you indicate you have a GenBank EMBL file then this panel will not let you move for ward until you have entered the file name of your GenBank EMBL file in the Enter filename box When you use the Browse button to select the GenBank EMBL file click once in the Wizard panel to make it the active window Then click the Next button to go on to the next panel If you do not use the browse feature be very careful of spelling and capitalization errors as GeneSpring attempts to locate the file before it allows you to progress to the next panel 5 T
197. e containing the Master Gene Table This question and the next question are mutually exclusive you must have one of them in your genomedef file ORFs the complete file name of the file contain ing the Master Gene Table of all the genes ORFs genelist txt 3 Ifyou are using either a GenBank file or an EMBL file to define your genome enter the com plete file name of the file describing your genome This is necessary if you used a GenBank or EMBL file This question and the previous question are mutually exclusive One of the two is required GenBank the name of the GenBank EMBL file describing this genome GenBank ecoli gbk Or GenBank ecoli ebl Even if you are using an EMBL file the object name in this entry is GenBank 4 If you have a file containing extra genes enter the complete file name of the file containing these supplementary elements This line is optional but must be included in the genomedef file for GeneSpring to incorporate this data nonORFs the complete file name of the extra file containing other genomic elements than in the ORFs file nonORFs extragenes txt Copyright 1998 2001 Silicon Genetics Appendix l 2 Installing a Genome from a Text File The genomedef File 5 Ifyou have a file containing the sequence data for the genome enter the complete name of that file including the seq suffix This line is optional but must be included in the genomedef file for GeneSpring to inco
198. e entry Sometimes the control channel value is very low and would artificially inflate the noise for its gene indicate the minimum value you would be willing to divide a gene s signal by NormalizeMinControl the minimum signal value to be used as a reference value for normalization purposes NormalizeMinControl 10 If you do not enter this line in your experiment file and you do have control channel values GeneSpring will automatically use the value given here 10 as the default cut off value Appendix J 15 Copyright 1998 2001 Silicon Genetics Installing from a Text File Normalizations Positive Controls 28 If you have control channel values for your experiment but the column containing the raw data has already been normalized using this information for example your data is reported in ratio form you can tell GeneSpring this using the line illustrated below If you have the raw data from both the gene and its control it is suggested you let GeneSpring perform your normalization rather than using this option For example Incyte data is reported in what they call ratio form but the ratio reported is not actually the gene s signal divided by its control in this case it would probably be better to use the raw signal and control values and let Gene Spring perform the normalization If you want to go ahead and use previously normalized data as your raw data you should still tell GeneSpring in which column s the co
199. e expression analysis were described by Tamayo et al 1999 GeneSpring s self organizing map algorithm begins by creating a two dimensional grid of nodes in the space of gene expression In each iteration one gene is selected and all of the nodes within a user defined neighborhood are moved closer to it This process is repeated with each gene in the selected gene list until the maximum number of iterations has been reached With each itera tion the neighborhood radius is incrementally reduced and nodes are moved by smaller and smaller amounts to produce convergence In this way the grid of nodes is stretched and wrapped to best represent the variability of the data while still maintaining similarity between adjacent nodes After the iteration is complete genes are assigned to the nearest node and a display grid of gene expression graphs is generated corresponding to the initial grid of nodes To Create a Self Organizing Map 1 Select Tools gt Clustering The Clustering window will appear Under Clustering Method select Self Organizing Map from the drop down menu 2 Choose a gene list from the Gene List folder in the mini navigator right click the list and select Set Gene List To remove a gene list select the list in the Genes to Use box and click Remove e To restrict the genes in the selected list right click an experiment or gene list in the navi gator and select a restriction For information on restrictions an
200. e multiple testing adjustment is now based on N 1 This process is continued as long as genes pass the successive tests This pro cess controls the FWER and expected number of genes by chance is a Westfall and Young permutation This procedure estimates the significance levels of each test by a nonparametric permutation calculation based on the distribution of the significance levels across all possible reassignments of samples to groups For small numbers of permuta tions all permutations are examined If there are more than 1000 possible permutations 1000 of them are selected randomly P values are evaluated with respect to this distribution using a step down procedure as in the Holm procedure This procedure controls the FWER and the expected number of genes by chance is a This test accounts for the dependence structure between genes and should give a more powerful test than the Bonferroni or Holm procedure However the permutation process takes much longer to calculate Benjamini and Hochberg false discovery rate In contrast to the above procedures the Benjamini and Hochberg procedure controls the false discovery rate FDR defined as the proportion of genes expected to occur by chance assuming genes are independent relative to the proportion of identified genes Expected number of genes by chance is a times the num ber of tests found significant after applying this correction There is no way to calculate this in advance so the state
201. e name in the same column you may use the general object name given above rather than entering the column number of the gene name for each data file If you have more than one data file with different col umn layouts and they have different columns containing the gene name use the object name given below If you are doing this make sure to indicate the column containing the gene name for every sample Experiment GeneColumn number of the column the gene name is found in for the experiment indi cated Experiment1GeneColumn 2 Experiment2GeneColumn Experiment3GeneColumn 2 w Explain to GeneSpring how to locate only the Gene Name These questions are only applicable if the column containing the gene name contains other nota tions as well notations not occurring in the list of genes defining the genome If column contain ing the gene names in your data file s only contains the gene name as it appears in the table of genes file or the GenBank EMBL file defining this genome skip these two questions and do not enter the lines associated with them in your experiment file 13 GeneSpring can remove a set suffix from a gene name A set suffix is a fixed string of charac ters which appear frequently at the end of your genes RemoveGeneSuffix exact suffix you wish removed from the gene nam RemoveGeneSuffix _at 14 GeneSpring can remove the entire notation following a slash including the slash itself To do this en
202. e on a selected gene list then the gene will be colored according to its expression level See the example of the mitosis pathway in Figure 3 11 e To add a gene to the pathway hold Ctrl and drag mouse over the desired placement area Type a gene name or keyword If a keyword is used select the gene from the resulting list e To delete a gene from the pathway right click over the gene and select Delete Pathway Element Zooming coloration movement and the Find Genes Which Could Fit Here features work in this view Find Genes Which Could Fit Here suggests genes that might be appropriate in certain areas of the picture Please refer to the Pathways chapter for more details Copyright 1998 2001 Silicon Genetics 3 23 Viewing Data in GeneSpring Compare Genes to Genes Compare Genes to Genes The Compare Genes to Genes view allows you to observe the similarity between the expression profiles of two genes in one list or in two separate lists Genes being compared are listed along respective graph axes The correlation between any two genes is shown by a colored square at their point of intersection Strong correlations in expression level are shown by a higher intensity color weak correlations by a lower intensity color Associated values for gene lists are shown as lines extending perpendicularly from each axis The length of the line represents the magnitude of the associated value You can view these associated values by zooming in on
203. e one of four options e Update genes from Silicon Genetics Retrieves gene information from the Silicon Genetics Mirror Database The mirror database caches information from GenBank Locus Link and UniGene to ease the load on the NCBI server and allow you to update faster Ifa requested gene is not found in the mirror database or if the information was cached more than 30 days ago the mirror server will update the information from all three databases e Update genes from GenBank Allows you to retrieve information on genes from Gen Bank e Update genes from LocusLink Allows you to retrieve information from LocusLink e Update genes from UniGene Allows you to retrieve information from UniGene The Update Genome window will appear 2 Select the column containing GenBank accession numbers from the pull down menu 3 To update information in places where data already exists select the Overwrite Exist ing Information checkbox If you leave this box unchecked GeneSpring will only add new information to blank fields When you update annotations GeneSpring creates a back up file of the pre update master gene table 4 Choose where you wish to save your annotations The default location is the master gene table you are currently using For some genomes you will have the option to save gene and non gene information in different places Updating from Silicon Genetics or GenBank will give you the option to retrieve sequence data Updating from
204. e per line e The name of gene must be in the first column e The following columns are data points for each parameter Copyright 1998 2001 Silicon Genetics Appendix F 2 Copying and Pasting Experiments Preparation for Pasting Experiment Parameter Values Name First Parameter Name with units Normalized Data gt gt a gt gt gt gt 7y y gt y gt y gt e OY oe oe oe ot Fos FY FF gt Y yo St Fe Fo Ce gt r YR YF FY F FFT Disease j no gt hepatitis gt hepatitis gt syphjli gt osteoporosis gt arthritis cancers cancer canc cancer gt arthritis arthritis gt arthritis gt arthritis gt hepav tis gt hepatitis gt hepatitis hepatitis gt hepatitis hepatitis hepdtritis gt hepatitis gt osteoporos osteoporosis gt osteoporosis osgeoporosis gt syphilis gt syph syphilis syphilis Infectious Disease j 7n y gt gt n gt n gt n gt n gt n gt gt n gt n gt n gt n gt FY gt gt y gt y gt y gt y gt FY gt gt n gt n gt n gt y gt FY gt Hepatitis gt n gt y gt y y gt n fh o gt b gt HO Uh lS n gt n gt n gt y gt FY gt gt y gt y gt y gt y gt Nn gt n gt n gt n gt n n gt n n gt Type Hepatitis gt n gt a gt n i oal a Ha a M gt n gt n gt n gt n gt bb b gt b gt a gt a gt a gt a gt gt nae nr a o HD DT nf Ca
205. e table a Select the Next button to continue The RT PCR Experiments panel will appear This panel tells GeneSpring whether the data you are loading comes from a RT PCR experiment RT PCR is a technology for measuring expression levels it reports these measurements in a different form than the standard array technologies Instead of reporting expression values it reports log expression value If you have not dealt with RT PCR experiments or have not heard of them before leave the No circle selected and proceed to the next panel If you are using RT PCR technology select the Yes circle a Select the Next button to continue Appendix D 12 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard 21 22 23 The Normalizations Negative Controls panel will appear This panel tells GeneSpring if you have any genes designated as negative controls on your array and if you want to normalize your sample using this data You typically have negative controls when there is DNA from a different genome than the one you are investigating on the array To indicate you have nega tive controls to use for normalizing select the Yes circle This normalization method takes the average signal intensities for all of the negative controls and subtracts this number from the signal intensity of each gene For more info about this normalization option see Normal izing Options on page G 1 If you do not
206. e taken A parameter is used to describe the condition or conditions in the experiment See Definitions of Parameters on page 2 11 for a more through description of parameters Parameters The number of parameters Parameters 4 4 Name the parameters Parameter Name Name of the indicated parameter Make sure to name each of the parameters enumerated in question 3 ParameterlName Kryptonite concentration Parameter2Name Variety of yeast Parameter3Name Test repeat number Parameter4Name Andromeda Strain infection Define Your Parameters In number 4 of section Define Your Experiment on page J 1 you named and numbered each parameter They will be referred to by their number for the remainder of this example For reasons of brevity the questions in this section are all phrased in reference to parameter 1 but you should answer each question for every parameter enumerated in question 4 5 If there are units associated with parameter 1 name them Parameter Units name of the units associated with the indicated parameter If a parameter does not have a unit name associated with it either do not enter the line Parameter Units for the parameter without units or enter the object name Parameter Units and the space colon space but leave the name of the units the object value blank ParameterlUnits ppm Parameter2Units Parameter3Units Parameter4Units 6 Is parameter 1 defined by a number i
207. e terms array chip and sample can be considered synonymous Array Layout synthetic picture of genes on arrays The Array Layout view can be used to check for gross slide related problems C Chip the measurements from a glass slide containing DNA samples for microarray analysis Classification a grouping of genes by k means or SOM clustering that is stored in the Classifica tions folder Classification View allows you to visualize one condition or experiment by organizing the genes according to previously defined functional categories or by some other previous knowledge of the genes For example of you have genes arranged into many lists in the same folder you can use that folder to categorize the genes on screen Colorbar the rectangle on the far right of the main GeneSpring screen The intensity of the color bar in GeneSpring indicates how reliable the data for each gene is Indicate a raw signal strength value to be considered very reliable a high signal strength value an average a medium signal strength value and an unreliable a low signal strength value Any gene with a signal strength control above the value indicated as a high signal strength will be colored using the brightest color appropriate any gene with a signal strength below the value given for unreliable data will be almost black in color The medium signal value gives the value for the mid point of the color bar and genes with a medium signal strength are colo
208. east Genes all genomic elements Fie Edit Yiews Experiments Colorbar H Gene Lists EH Gene Ontology PIR keywords all genes all genomic elem 3 ACGCCT in all OF L8 like YMR199W C EHS Experiments Hi Random Data tir Tools Annotations Window Bea Yeast cell cycle ti HA een Trees EH Experiment Trees E Classifications EH Pathways FH Array Layouts EH Drawn Genes Gt External Programs Bookmarks EH Scripts 100 time 20 0 minutes Magnification 1 gt l Figure 3 3 The Bar Graph view Figure 3 3 shows a Yeast cell cycle time series in Bar Graph view Copyright 1998 2001 Silicon Genetics time tr D GE 120 140 160 1O x Sso on nO o xm Trust Animate 3 8 Viewing Data in GeneSpring Classifications View Classifications View This view allows you to visualize an experiment or a set of experiments by organizing the genes according to previously defined categories To use Classification view 1 Select a gene list 2 Classify the genes using one of two methods a Right click a subfolder in the Gene Lists folder and choose Use as Classifica tion from the resulting pop up window b Select a previously created classification from the Classifications folder in the navigator see Clustering and Characterizing Data in GeneSpring on page 5 1 Color genes by your chosen classification 1 Select Colorbar gt Colorby classification 2
209. eastRB txt Yeas tRC txt YeastRD txt The Region Designation File s If there is more than one region to which the genes from a sample could belong then the region must be noted somehow in the experimental data file If the region is noted in the experimental data file as either a unique entry in its own column or as a suffix appended to another column s entry as is common with Affymetrix chips then you should create separate region designation files one for each region In this region designation file should be one line reading RegionSuffix character or string of characters used either as a unique column entry or as a suf fix This string designates a particular region RegionSuffix A All of the entries in the region column designated in the htmi file or in the Regions Normaliza tion panel of the Experiment Wizard having the same suffix as the object value indicated after one of the RegionSuffix entries are considered to be in the same region For example if there are four regions A B C and D there will be four region designation files each with one of the lines RegionSuf fix RegionSuf fix RegionSuf fix RegionSuffix awp Appendix K 4 Copyright 1998 2001 Silicon Genetics Experiment File Formats What format does this data need to be in Given a region column in the experimental data file containing these entries GenelA Gene2B Gene3C Gene4D Gene5A Gene6B Gene7C Gene8sD Gene9A
210. ecific The separation ratio determines how large the correlation difference between groups of clustered genes has to be for them to be considered discrete groups and not be lumped together This num ber should be between 0 and 1 5 3 Copyright 1998 2001 Silicon Genetics Clustering and Characterizing Data in GeneSpring Trees It is not normally appropriate to change separation ratio or minimum distance Separation Ratio The separation ratio determines how large the correlation difference between groups of clus tered genes has to be for the groups to be considered discrete groups and not be joined together e Increasing separation increases the branchiness of the tree e Default Separation ratio is 0 5 Separation ratio can range from 0 0 to 1 0 e Ata separation ratio of 0 all gene expression profiles can be regarded as identical To change the maximum correlation number highlight the number in the white box next to the Separation Ratio label and type in a new value You will not normally want to modify value Minimum Distance The number specified in the Minimum distance box determines the minimum separation con sidered significant between genes This reduces meaningless structure at the base of the tree The minimum distance deals with how far down the tree discrete branches are depicted A higher number will tend to lump more genes into a group making the groups less specific e Decreasing minimum distance increases the
211. ecting this will launch your browser and take you to http www sigenetics com GeneSpring index html There should be manuals and information on workshops designed to help you use GeneSpring more effectively GeNet Database Selecting this item will launch your browser and take you to a webpage describing GeNet You can download a demo copy of GeNet from that page You will also see other commands to upload or download with GeNet Please see Publish to GeNet on page 6 6 or the GeNet User Manual Register for a Workshop Selecting this will launch your browser and take you to Silicon Genetics training page Here you can take advantage of Silicon Genetic s many training options System Monitor This item will bring up the Java System monitor with information about free memory and what is currently happening on your computer If you are running low on memory GeneSpring will bring up a warning box About Selecting Help gt About will bring up the initial graphic of GeneSpring showing you the ver sion number demo expiration date and other useful information Also only for Macintosh users there is a confirmation dialog appearing at the closing of the last browser window Copyright 1998 2001 Silicon Genetics Appendix A 2 Preferences Window Data Files Appendix B Preferences Window The preferences screen allows you to change GeneSpring s global preferences Note that some changes may not take effect in the currently o
212. ed pathway Publish to GeNet Uploads your information and the pathway picture to GeNet see Publish to GeNet on page 6 6 Delete Pathway Lets you delete a pathway A confirmation dialog box appears Rename Pathway Allows you to rename your pathway Regulatory Sequences The Find Potential Regulatory Sequence window allows you to find common regulatory sequences within genes in a gene list or to search for a known sequence It also compares the fre quency of occurrence against all other gene lists in the genome This feature is useful for finding genes sharing similar regulatory sequences or having a particular regulatory sequence in common When the regulatory sequences tool compares genes to the remainder of the genome it uses the all genes list The all genomic elements list includes non gene elements that are not expressed In GeneSpring version 4 0 and later the sequence information will be loaded automatically Note You can change the load automatically feature by going to Edit gt Preferences gt Genome Array View and remove the check from the Load Sequence checkbox S Find Potential Regulatory Sequences aa a oj x EH Gene Lists Find new sequences Results EHC Gene Ontology View Genes for Selected Rows Details gt gt PIR keywords Search Criteria Sequence Observed g all genes Gene List all genomic elements EE all genomic elements Search before ORFs z 3 ACG
213. ee Updat ing your Master Gene Table with GeneSpider on page 2 15 for more information 11 Synonym This column allows for other names to be entered for the genes Multiple names should be separated by semicolons 12 Sequence The sequence data if known 13 PM The Public Medline accession number if known Multiple identifiers should be separated by semicolons 14 custom1 Not specified This column will not be interpreted by GeneSpring but it is useful for some reports 15 custom2 Not specified This column will not be interpreted by GeneSpring but it is useful for some reports 16 custom3 Not specified This column will not be interpreted by GeneSpring but it is useful for some reports 17 Type A result of the conversion from a gbk file to a master table of genes It come from the GenBank column feature type For example possible entries include CDS gene terminator rRNA 18 Database reference also called DBid A specific field returned by the GeneSpider There are dbxref entries in GenBank and these entries give database ID for other non GenBank databases such as the SwissProt ID numbers There may be multiple entries for each gene Copyright 1998 2001 Silicon Genetics Appendix H 3 Creating Folders for New Genomes Raw Data The Mapped format allows you to link up to three different names plus three more custom names for the same gene Using this method you could query one ge
214. ee a pop up window with a message to that effect e Rename Selecting this will result in a new window asking for the new name Type in the new name and click OK Appendix P 7 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Navigator e Publish to GeNet This will bring up the GeNet UpLoad Window From here you can load data from this list into the GeNet database Please see Publish to GeNet on page 6 6 or the GeNet User Manual for more details e Save to disk This feature will save any data object to your local drive if it is not already there Typically only if you are working from a server or from GeNet will this be a useful option The Main Folder Pop up Menus A right click over a main folder such as Gene Lists or Classifications will produce a small menu possibly including some or all of the following Mac Users should use Control Click to activate pop up menus e Use As Classification This command will shift your current view into classification if you are not there already and list the genes under each classification heading The coloration will not change See Classifications View on page 3 9 for more information e Use As Coloring This command will change the current coloring of your view to a colora tion scheme reflecting the folder chosen The colorbar will change to a list of blocks with cap tions telling you which list is which See Color by Classification on page 3 34
215. ef file are searched for experiment data gene lists classifications and so forth As the local directory must be indicated in the shared directory every user in your group must keep their local directory in the same place on their local computers In the exam ple this place would be the C Silicon Genetics GeneSpring data Ecoli 13 If there is a prefix a string of characters prepended to the start of your genes systematic names you can tell GeneSpring to disregard this first part of the gene name and not display it This line is not required and it is rarely used SystematicPrefix a string that is often prepended to the start of gene names and should be ignored if seen SystematicPrefix ecoli 14 If you wish the genes systematic names to appear entirely in upper case letters GeneSpring can convert them to this automatically This line is not required and is rarely used ForceUpperCase set to true if you want all the names of the genes converted to upper case set this line to false otherwis ForceUpperCase true 15 If you wish the genes systematic names to appear entirely in lower case letters GeneSpring can convert them to this automatically This line is not required and is rarely used ForceLowerCase set to true if you want all the names of the genes converted to lower case set this line to false otherwis ForceLowerCase false Appendix l 6 Copyright 1998 2001 Silicon Genetics Inst
216. efining the genome In this case you must specify a text file describing the map see How to describe a map on page 7 Map mapA txt Appendix K 6 Copyright 1998 2001 Silicon Genetics Experiment File Formats What format does this data need to be in 4 The regions are defined by file name extension The experimental data for each region is in a separate file The file names for each sample specified in the Experiment Wizard or in the html file are base names and each region adds an extension to this file name To prevent name conflicts this option is frequently used with the map option FileNameExtension chipA How to describe a map Maps are used when you want to change gene names from the raw names e g chip coordinates into more standard gene names They can also be used to specify a list of genes defining a region A map file is a text file containing just two lines FileName GeneList txt ChangeNames true The FileName entry specifies the name of a text file containing one line per gene If Change Names is true then the text file should consist of two columns separated by a tab The first col umn should be the gene names as they appear in the experiment data file the second column should be the gene names as they appear in the list of genes defining the genome If Change Names is false then the text file should only have one column In this case the map is used only to specify what is pre
217. ement system such as Windows Explorer For example a new folder named Mouse has would be created and placed into the data directory of GeneSpring Before your new Mouse folder will appear in GeneSpring navigator you will need to create a cor rect mouse genomedef file A genomedef file will contain all the information GeneSpring needs to create a folder and other data objects Make sure you save the genomedef file in the correct folder the Mouse folder after you create it Please see The genomedef File on page I 1 for details on creation Raw Data What Data Are Necessary You must have a list of distinct names for all the genes you intend to work with In addition a genome may also have GenBank Accession Number sequences alternative names functional information map positions EC numbers and so on associated with genes It may also include links to web based databases Each genome should have a distinct name to reduce confusion What Format do these Data Need to be in Your Master Gene Table file You will generally need either a Master Gene Table or a GenBank EMBL entry for your organ ism If you use a Table of Genes containing the genes GenBank Accession Numbers then the GenBank information associated with each gene can be automatically updated See Updating your Master Gene Table with GeneSpider on page 2 15 for how to do this There are four possible formats for a Master Gene Table name li
218. ement relies upon having the default directory set to GeneSpring data as part of the GeneSpring setup This allows you to avoid having to write out the full path names of the Runsas bat and Fastclus sas files within FASTCLUS programdef as long as they are placed in the GeneSpring data directory The programdef file must be in the Programs subfolder of GeneSpring data directory If you don t already have a Programs subfolder in this directory create one The code following the title and location of the file should be entered as the text of that file In the GeneSpring data Programs put this file GeneSpring data Programs FASTCLUS programdef External Program interface for SAS Name FASTCLUS Command runsas bat fastclus expt txt clus txt Input 4 Output 6 This file defines four things see the External Program Interface FAQ for details e The displayed name in GeneSpring e The input format for the experimental data going into SAS e The output format for the cluster membership data coming back from SAS e The name of the batch file actually doing the work Copyright 1998 2001 Silicon Genetics 4 42 Analyzing Data in GeneSpring External Programs In the GeneSpring data directory place these two files GeneSpring data Runsas bat echo off set infile 2 set outfile 3 cat exe gt 2 C PROGRA 1 SASINS 1 SAS V8 SAS EXE 1 sas nologo config C PROGRA 1 SASINS 1 SAS V8 SASV8 CF
219. endor API you lock yourself into using that vendor s DBMS However your programs will be efficient as possible The second type of CLI is a standard or open API which is supported by more than one database vendor Several open database APIs are available one of which is ODBC ODBC is a standard CLI for accessing SQL databases from Windows The Genetic Analysis Technology Consortium The Genetic Analysis Technology Consortium GATC was formed in an attempt to standardize the rapidly growing field of array based genetic analysis The consortium was created to provide a unified technology platform to design process read and analyze DNA arrays The goal of the GATC is to make micro arrays broadly available and provide a technology plat form that allows investigators to use components from multiple vendors Copyright 1998 2001 Silicon Genetics Appendix E 2 Installing from a Database Adding an Experiment from a Database Databases and GeneSpring Experimental data is not always stored on the researcher s desktop in simple text files Sometimes the data is stored on a relational database GeneSpring can save and load all types of data to an SQL database through ODBC Experimental data can be loaded from a database simply by telling GeneSpring which table s contain the data and which columns contain the experimental index You then load in the data using the Experiment Wizard almost exactly as you would if they were text files see En
220. ene command or type Ctrl I This command brings up a window with more detailed information about a particular gene For more information see the Gene Inspector on page 3 37 Close the Gene Inspector by clicking the Cancel button e Zoom In This command allows you to have a closer look at a particular section or point within the browser Zooming is accomplished by clicking in the upper left corner of the region you wish to enlarge and dragging the cursor to the lower right corner Repeat until the desired magnification is reached Systematic and then common gene names if they exist are listed beneath the gene as soon as there is adequate space under their associated rectangle Sequence information is not visible in the Gene Inspector e Arrow Keys When the genome browser is magnified by Zooming the arrow keys on the keyboard allow you to shift the particular section being displayed in the direction of the arrow pressed e Page up Page Down Like the arrow keys except over a larger scale the Page Up Page Down keys on a typical keyboard allow you to vertically pan through the genome browser Appendix P 1 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Drop Down menus Common Commands in the Drop Down menus The File Menu Print You have several options on how to print from GeneSpring or save graphics as a file New Genome or Array This command will allow you to select from a submenu of available gen
221. ene ontology based on keywords from annotations in public databases The classification scheme is derived from Gene Ontology consor tium gene lists Additional functional classifications were constructed by Silicon Genetics Global Error Models Using the Global Error Model allows you to produce a better estimate of precision You can use these estimates in a number of analyses in GeneSpring including filtering and clustering Copyright 1998 2001 Silicon Genetics 1 4 Introduction New in Version 4 0 Statistical Group Comparison You have three options when choosing Statistical Group Comparison e Parametric test assume variances equal Student s t test ANOVA e Parametric test don t assume variances equal Welch t test Welch ANOVA e Non parametric test Wilcoxon Mann Whitney test Kruskal Wallis test Class Predictor The Class Predictor feature allows you to predict the value or class of an individual parameter in an uncharacterized set of samples using a training set where the parameter values are known New Inspectors You can now view at a glance all the data for a particular experiment condition interpretation and classification Include Attachments You can now attach any sort of file to a gene list experiment or classification Merge Split Experiments You can now merge experiments or individual conditions and split experiments Customized Clustering Annotations GeneSpring 4 1 allows the user to def
222. enes This will not affect the inter pretation of your data although it might help you to make genes more visible on screen or make it easier to print screen shots 1 SelectEdit gt Preferences 2 Inthe drop down menu select Colors 3 Select the type of information whose color you would like to change and click the Change button 4 Adjust the slider until the color you want is displayed in the preview window at the top of the Preferences window 5 Click OK For more details about the other options in the Preferences window please refer to Preferences Window on page B 1 The Inspectors GeneSpring s Inspectors are a series of windows allowing you to view the current defaults and available details of any gene condition classification or experiment Gene Inspector One of GeneSpring s most flexible tools the Gene Inspector allows you to look at all the data associated with a particular gene see the lists that include your gene make correlations and link directly to Internet databases In the upper left corner of the Gene Inspector window is the name of the gene and an area for notes The table in the upper right corner displays the normalized control and raw values as well as the t test p value and flag for each measurement In the center of the window is a browser showing a graph of the gene across all conditions At the bottom of the window from left to right are correlation functions lists containing yo
223. enes Analysis Tools on page 4 1 repec tivily Each measure takes two expression patterns and produces a number representing how similar the two genes are Most of the measures of similarity are correlation measures and their value will vary from 1 exactly opposite to 1 the same For a measure of distance the result will vary from 0 the same to infinity different For confidences the result will vary from 0 no confi dence to 1 perfect confidence Both distance and confidence are actually measures of dissimi larity small means close and large means far away These are each transformed to measures of similarity by GeneSpring in ways detailed below If one expression value for a particular experiment for either gene is missing that experiment will be not considered in the calculation The notation used to describe the formulas e Result the result of the calculation for genes A and B e n the number of samples being correlated over e a the vector a a2 a3 a of expression values for gene A e b the vector b b gt b3 b of expression values for gene B Normal mathematical notation for vectors will be used In particular e ab a b a7b gt a b e a square root a a Appendix L 1 Copyright 1998 2001 Silicon Genetics Equations for Correlations and other Similarity Measures Common Correlations Common Correlations Standard Correlation Standard correlation measures the angular separation of
224. enini E E a A E aaa L 3 Two sided Spearman Confidence ssssseseesessseesseesresresseesresresseeseesrosseeseseesseesse L 3 DDIStATIC Eg cases 53 cigs dus ep pic tasks a aaia go aa a aaa a i a aaien L 4 Special Cas Correlations soniers ia n a a a E L 4 Smooth Correlation sn siczs cai Peas geacen Aaa pevvig ves cnd aoap vas ta vedang s inii a iis naeia L 4 Change Corel At On miimi ieoten e a eA E EAE aO nA i aate L 5 Upregulated Correlation ssssesessssessessesssessessessresstestssresseestesrssessteseserssresseseest L 5 Appendix M Creating an Array in GeneSpring ssesssecsssooesooessoesssesssesssoossoossossssee M 1 Examples of Jayout files for Arrays cccscccesscssccesecessscsesscssccssccesscesensenceeees M 2 Appendix N Technical Details on the Statistical Group Comparison 00 N 1 Fot Each Gene ene eae me a meee eR Pe on eee N 1 IR CSTE TIC OS ccetarar es eE E E A wtescusash ues aac A A a E N 4 7 Copyright 2000 2001 Silicon Genetics Appendix O Technical Details for the Predictor cssccsssssssscssesssssssssssssoesees O 1 Gene SCLC CUOT hrena E aE a Ea i aaa a aa aaa O 1 Classifying the Test Samples a seas saci asnasut vastgoeioscaeseedeotennecanceges dieabencdecusunseeleaeseediadse O 1 Decision Threshold sacisthiccteuns cd tonts eet cat a ies tet E Pal Melia Sut ta aatee Rd O 1 References for the Predictor 5d ccccrc ys Seas ceasavatsansauciaeSeares os eset haeecese teas tli ease eas
225. ensitive so please use the capitalizations as presented here e Name The name of this layout to appear in the navigator window of GeneSpring e Icon optional The path of a 16 by 16 gif file to appear next to the layout in the navigator window e VerticalSubArrays optional default 1 The number of rows of sub arrays e HorizontalSubArrays optional default 1 The number of columns of sub arrays e HorizontalPerSubArray The number of columns of dots in a sub array e VerticalPerSubArray The number of rows of dots in a sub array e VerticalDuplication optional rarely used When dots are duplicated vertically the number of copies e HorizontalDuplication optional rarely used When dots are duplicated horizontally the number of copies e CommondArrayType The format of the array e Q X Y The data file contains two columns The first is a list of genes the second is a set of three numbers separated by commas or hyphens The first is the sub array number the second is the X coordinate and the third is the Y coordinate All numbers start count ing from 1 The subarrays are counted left to right top to bottom The second column can optionally be enclosed in quotation marks e Q R C Same as Q X Y except the X and Y coordinates are swapped e CLONTECH LNL There is no datafile All genes have systematic names of the form B4c indicating where they are in the array The first capital letter indicat
226. entromeres or genes from strains differing slightly from the sequenced strain To tell GeneSpring where to find the additional elements a Click the Yes circle to select it If you do not have a separate table of genes file leave the No circle selected and go to the next panel b Either click in the Enter Filename box and type the complete file name and pathway or click the Browse button to select a file Look at the listed folder to make sure you are in the correct directory c Click the table of genes file containing the extra genomic information d Click the Open button This will insert the file information into the Enter Filename box e Click the arrow to the right of the Select a file format box A menu will appear f Click the format used in the supplementary table of genes file For a description of the four format options see section What Format do these Data Need to be in on page H 1 Once you indicate you have a file containing extra genomic elements you cannot proceed to the next panel until you have indicated a file and a file format Beware of spelling and capital ization errors when indicating the file name and pathway as GeneSpring checks to make sure the file you name exists before letting you go on to the next panel 8 The Links to Web DataBases panel will appear This panel allows you to link GeneSpring directly to web based data sources on your genes You can create a link to a URL containing the name of o
227. ents gt Merge Split Experiments 2 To merge experiments conditions open the Experiments folder in the mini navigator and click on the first experiment folder experiment or condition you would like to merge find a condition by clicking on the plus sign next to the experiment icon e Click the Add button e Repeat steps 3 and 4 below until you have added all your experiments conditions To Split Experiments Conditions 1 SelectExperiments gt Merge Split Experiments 2 Open the Experiments folder in the mini navigator and click on the first experiment condition you would like to delete e Click the Add button e Individually select the experiments conditions you would like to remove and click the Remove button 3 Click OK The Experiment Parameters window will appear You will see a parameter called Experiment listing the names of the experiments involved You can alter add or delete param eters For information about the functions in this window see Change Experiment Parame ters on page 2 8 4 Click Save The Choose Experiment Name window will appear 5 Enter names for your experiment and experiment folder and click Save You will find your merged split experiments in your Experiments folder 2 6 Copyright 1998 2001 Silicon Genetics Creating DataObjects in GeneSpring Creating a Genome through the Autoloader To Duplicate an Experiment 1 SelectExperiments gt Duplicate Experiment Right click the experiment n
228. er the line Layout name of layout file once You may have entered this file already please refer to The required layout file for Region Specifications on page J 9 Layout complete name of the layout file Layout AffyYeastLayout4 txt Normalizations Control Channel Values If you do not have control channel values skip these questions and the associated experiment file entries 26 If you have a control channel value for each gene to indicate the trust you have in the experi 2J mental data for each gene you probably want to normalize the genes by dividing their control strength by the control channel s control strength If you have a background signal for either or both of these values it is subtracted from the signal intensities before they are divided For more information on this normalization option see Normalizing Options on page G 1 If you wish to use this normalization enter true as the object value in the line illustrated below If you do not have control channel values or you do not wish your data to be normal ized using the control channel values either do not enter the line NormalizeToReference or enter false as the object value in that line Control channels generally apply to two color experiments NormalizeToReferenc ither true or false NormalizeToReference true If you do not have control channel values skip this question and the associated experiment fil
229. eriment F4 Microsoft Excel Diseased Data xls File Edit View Insert Format Tools Data Window Help oe B a E F WL Multiple Disease Example CAA Sick Tho y y S Disease no epatitis f hepatitis fyphilis osteop ectious Disease y n Hepatitis O ype Hepatitis Q Cancer ype Cancer Q Time minutes 0 10 z 30 YALOO1C 0 941667 0 575 0 95 0 925 1 166 YALOO2VY 1 738318 0 971963 0 570093 0 635514 1 074 YALOOSVY 0 710966 0 68773 0 964863 0 679229 1 105 Figure E 1 Example of parameter arrangements and values hi h y y a n n sis is isis sis isis First gene in lis e In the first column is the name of the parameter e Subsequent columns have values for parameter in that sample e Each parameter must have units in parentheses in the same column as the name For exam ple the parameter time would be immediately followed by minutes If your parame ters have no units you must follow the name with an empty set of parentheses or GeneSpring will not recognize it as a parameter Appendix F 1 Copyright 1998 2001 Silicon Genetics Copying and Pasting Experiments Preparation for Pasting e Asa default GeneSpring assumes that the parametric values to follow are numeric and to be displayed in numerical order If the parametric values for a parameter are non numeric immediately after the unit indicating parentheses empty if no units enter an asterisk There should be a space between right
230. eriment Inspector if the experiment has parameters with names matching all fields in the URL In both cases the parameter names are not case sensitive so if an experiment has a parameter called Time you can specify it as lt time gt lt Time gt or lt TIME gt in the URL and they will all work ExperimentHypertextLinks Links to external web based databases You can have more than one of these lines you should have one line for each link ExperimentHypertextLinks linkname http www somewhere experimentlikemine lt system atic gt amp id lt time gt 11 Use this line if there is a particular experiment you would like GeneSpring to automatically display in the genome browser when you open this genome This genomedef entry is optional if it is not included GeneSpring will open the genome but not open any particular experiment when you select this genome to be displayed defaultExperiment the name of the default exper iment you want started when opening this genome defaultExperiment yeast extraterrestrial studies The name following the object value should be the same name given to the experiment in the name line of its html file and or it should be the name entered for the experiment in the Prop erties of an Experiment Set panel of the New Experiment Wizard Both of these options are case sensitive so make sure the spelling and capitalization is correct See The Experiment Wizard on page D 1
231. es Analysis Tools saien asa aa AA a anaa aiei 4 1 Restrictions Over an Entire Experiment or Interpretation 0 0 0 0 ceeeeereeeeeeees 4 3 Restrictions over a Single Condition or Sample ccccccesceeesceeseeeteceeeeeeeeenaees 4 7 Restricting by Associated Numbers csccescesscesseeeeeceseceseeeseecaecneeneeeensees 4 9 New Gene List window sinc asia ashe eiar a iets aso taiwan eae aerate 4 11 Making Lists with the Find Similar Command 2 0 0 0 ce eeeeseeeeceteceeeeneeeseeeeeeeeeeeees 4 13 Making Lists with the Complex Correlation Command cceccceeseeeseetteeteeeees 4 14 The Multi Experiment Correlation Window 0 ceceeceeseceteeseeeeeeeeeeeeeseeeenaes 4 15 Finding Offs t Genes 3 icc cater ecluadlesdeasin Anictss cae ccusacnuech tone eae ees cadheaaeieanne anaes atecunenes 4 18 Making Lists from Properties sc 5 soaute seek ase he dain aetek weeaniiyas i deasdc a ianiaieh xe 4 19 Making Lists with the Venn Diagram 0 cccecccecssecsseceseceeeeeeseecaeceeeeaeecsaeeneenaes 4 19 Making Lists from Classifications siss siscssissasccteas secnassbdsccde shsvsnataceaecayehseearsteniensetaade 4 21 Find Interesting Genes sisira ian a i n a e i e a aA 4 21 Making Lists from Selected Genes ais heasig ot tdns Seve ves taaedcca i otav es ands Gada eee Rees 4 22 Creating Diawn Genes sass xxdachsedadadalstautissacensesbandosesraaadaus bebe AKAOA S EESE aa ETARTE 4 22 Pathways tte ore E e a E A A N R 4 23 Importing a Pathway cbc S
232. es which sub array the number indicated which column and the lower case letter indicates which row e CLONTECH LNNL Same as LNL except there are two digits instead of one e DataFileName The name of a datafile linking locations with gene names in format given by the CommonArrayType choice In the second example below there are several lines of a DataFile file Appendix M 1 Copyright 1998 2001 Silicon Genetics Creating an Array in GeneSpring Once you are done creating the Jayout file you should save it in the ArrayLayouts folder of the genome folder for which the layout pertains For example if you have not changed the defaults set up of GeneSpring the path to the layout folder in the yeast genome would be C Program Files SiliconGenetics GeneSpring data yeast ArrayLayouts Examples of ayout files for Arrays Here is an example for Pat Brown s yeast layout Name Pat Brown Icon XXX gif VerticalSubArrays The following is from a file Pat layout s Yeast Layout 2 HorizontalSubArrays 2 HorizontalPerSubArray 40 VerticalPerSubArray 40 VerticalDuplication 1 HorizontalDuplication 1 CommonArrayType Q X Y DataFileName PatLocationList txt Following are the first few lines of the file PatLocationList txt YHR007C 1 13 1 YBR218C 2 13 1 YALO51W 1 14 1 YALO53W 2 14 1 YAL054C 1 15 1 YALOS5W 2 15 1 YALO56W 1 16 1
233. eter Name Parameter Value 1 elephants 2 2 elephants 34 2 daises 30 Table B 2 Sample table of mixed up parameters In Table B 2 you do not have parameters in the individual columns All parameters tables should have an associated sample number somewhere If you use a GATC database you will have to re link all the sample numbers to the parameter numbers In that case you need to define an SQL In that case you must define a SQL line to get those parameters for example SQLgetParameters select This should retrieve values of and names of the parameter Appendix E 6 Copyright 1998 2001 Silicon Genetics Copying and Pasting Experiments Preparation for Pasting Appendix F Copying and Pasting Experiments You can use the copy Ctrl C and paste Ctrl V functions to insert a new experiment or lists from the clipboard into GeneSpring This is a very quick but somewhat inflexible function of GeneSpring Preparation for Pasting You should have normalized data in an Excel file or saved as tab delineated text Figure E 2 You must have all of the following three parts to your data Your data must be in the following for mat to correctly paste into GeneSpring 1 Name e First line must be the unique name of the experiment 2 Parameters e The second line must be the first parameter you may have as many parameters as you want but you must have at east one The seven parameters The parameter values for this exp
234. expression vectors for Genes A and B around zero As almost all normalized values for genes are positive you find mostly positive cor relations between genes when you use the Standard correlation This metric is designed to answers the question do the peaks match up or to put it another way are the two genes expressed in the same samples Since these questions are the most frequent questions a biologist is trying to get answered GeneSpring calls it Standard correlation It is important to note what mathematicians and statisticians refer to as correlation usually refers to the Pearson correlation The Standard correlation would be called Pearson correlation around zero by mathematicians and statisticians This is how to compute a Standard correlation Standard correlation a b a b Pearson Correlation The Pearson correlation is very similar to the Standard correlation except it measures the angle of expression vectors for genes A and B around the mean of the expression vectors for example the mean of the expression values constituting the profiles for Gene A and Gene B Generally the mean of the expression vectors will be positive since expression values are based on concentra tions of mRNA Using the Pearson correlation you get more negative correlations then you get from the Standard correlation for example you find more genes that behave opposite to each other because of where you put the basel
235. f control samples indicate the sample numbers of the control samples Multiple sample numbers must be separated by commas e g 1 2 Ranges of sample numbers can be indicated by a dash e g 1 3 5 e Example 1 1 3 5 Translation normalize all samples to the mean of samples 1 2 3 and 5 Alternatively you can normalize subsets of samples to the mean of specific subsets of control samples Begin by listing those samples to be used as controls for a majority of the samples as described above For samples to be normalized to the mean of a different set of samples add in parentheses a list of sample numbers for the samples to be normalized followed by a colon fol lowed by a list of sample numbers for the control samples You may specify as many of these lists as you need e Example 2 1 5 4 Translation normalize all samples to sample 1 including sample 4 except for sample 5 which should be normalized to sample 4 Appendix G 10 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalizing All Samples to Specific Samples e Example 3 1 5 6 4 7 10 7 8 Translation normalize all samples to sample 1 except for samples 5 6 and 7 through 10 Sample 5 and 6 should be normalized to sample 4 and sample 7 through 10 should be normal ized to the mean of samples 7 and 8 e Example 4 1 2 3 5 7 3 4 6 8 9 5 Translation all samples will be normalized to the arithmetic mean of samples 1 and 2 except for samples 3 throu
236. f you want to normalize your data by making the median of all of your measurements 1 for each single sample in your experiment If you have not already preformed normaliza tions on your data you generally want to use this normalization option To indicate you want to normalize each sample to itself select the Yes circle Another question will appear Sometimes something will go wrong with the experiment and you will get very low values for everything Inthe Enter lower cut off value box indicate the cut off value This number will be used by GeneSpring to not raise all of the control strength values up to a median of 1 if their average is below this number For a mathematical illustration of this normalizing option please refer to Normalize Each Sample to Itself on page G 6 a Select the Next button to continue 25 The Normalizations Each Sample to a Hard Number panel will appear In this panel you tell GeneSpring if you want to normalize your samples to a value you enter You would normally only use this function if you have pre normalized data such as data prepared with Affyme trix s Global Scaling In that instance you would want to divide all data by 2500 or whatever number you chose to normalize by in the Affymetrix software You will need to do this because the GeneSpring analysis algorithms assume your data is normalized to a median of 1 a Select the Next button to continue Appendix D 14 Copyright 1998 2001 Silicon
237. file has specific requirements of what must be in it but the items can be in any order e jdbc odbc NameofDatabase e ExperimentTableName SampleName If the index and gene name are separate you will need more than one table This should be a one word name Case sensitivity depends on the database e ExperimentTableIndex which column contains the experiment number e GeneColumn the column number containing the gene names e IntensityColumn should contain actual results e debug true When true it will show what commands are sent to the database when you use the Experi ment Wizard 3 Arranging your Parameters You need to make an SQL command that will get the parameters in all samples You can use MicroSoftQuery in Excel to generate SQL commands e From Excel go to the Tools menu e SelectGet External Data e SelectNew Database Query e Make sure you tell it you want to edit in MicroSoftQuery Appendix E 4 Copyright 1998 2001 Silicon Genetics Installing from a Database Entering your Prepared Database into Gene Spring GeneSpring wants 1 Experiment ID 2 Another experiment ID must be unique 3 Other parameters Heading from tables name of column Double click headings to change the name if you want Button at the top of the query box says SQL Click it to get SQL statements SQL Get experiment and indexes SQL statements this needs to be on one unbroken line do not use word wrap in your text editor Stil
238. following options e Make list of these genes lists genes in the immediate geometric area e Make list of genes in both lists lists genes common to the two circles i e the intersec tion e Make list of genes in either list lists all genes in the two circles i e the union If you click in an area where three circles overlap you will have the following options e Make list of genes in all lists lists genes common to the three circles i e the intersec tion e Make list of genes in any list lists all genes in the three circles i e the union Copyright 1998 2001 Silicon Genetics 4 19 Analyzing Data in GeneSprin Making Lists with the Venn Diagram yzing g If you click a non overlapping gray area you can make a list of genes in that section only gt GeneSpring 4 1 Yeast Genes like MR199W CLN1 0 95 File Edit View Experiments Colorbar Tools Annotations Window Help Gene Lists 5 0 Normalized Experiments Gene Trees 4 0 Experiment Trees Classifications Pathways Array Layouts Drawn Genes External Program Bookmarks Scripts Make list of these genes es Make list of genes in both lists 19 genes Make list of genes in either list 62 genes o 30 70 time 0 minutes Animate Magnification 1 Zoom Gut ae OES CS ei Figure 4 3 A Venn diagram with pop up menu 2 Name and save your new list In views where lists can be ordered such as the Ordered List vie
239. for more information e Split Unsplit Window This feature allows you to view multiple graphs simultaneously in the genome browser You can also unsplit the window by selecting View gt Unsplit win dow e Publish to GeNet This will bring up the GeNet UpLoad Window From here you can load data from this list into the GeNet database Please see Publish to GeNet on page 6 6 or the GeNet User Manual for more details e Clear The command will clear the current display e Delete This command will delete the data object There will be a confirmation box The Gene Lists Folders Pop up Menus A right click over a subfolder in the main Gene Lists folder will bring up the following com mands e Use As Classification This command will shift your current view into classification if you are not there already and list the genes under each classification heading The coloration will not change See Classifications View on page 3 9 for more information e Use As Coloring This command will change the current coloring of your view to a colora tion scheme reflecting the folder chosen The colorbar will change to a list of blocks with cap tions telling you which list is which See Color by Classification on page 3 34 for more information Appendix P 8 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Navigator e Split Unsplit Window This feature allows you to view multiple graphs simultaneously in
240. for more information about entering an experiment If you do not know the name of any experiment done with this genome when you create it this line can be added or modified afterwards Just remember to save the modified genomedef file Appendix l 5 Copyright 1998 2001 Silicon Genetics Installing a Genome from a Text File The genomedef File 12 If you work in a group that is storing data and analyses in a shared environment usually this means that you have all of the data for the group in one file system you will probably also want to have your own local data for each genome A specific use of this is for gene lists not the genome defining Master Gene Table but a gene list you create within GeneSpring it is often desirable for each person to keep the gene lists they create initially separate as trial lists and then merge them into the groups permanent set when they are more certain about the sig nificance of individual lists To store data locally you specify in the genomedef file of each genome a second directory to be searched for experiment data gene lists trees etc This directory is specified with the line below This is an optional line HomeDirectory The complete path of an extra directory to search for to find information for this genome HomeDirectory C Silicon Genetics Gene Spring data Ecoli Including this line means that both this directory on your local computer and the directory containing the genomed
241. for you Click the Set Value Order button Select all the values you want to order so you can use the Sort Ascending or Sort Descending but tons The main GeneSpring window will sort your parameters according to the new system Sorting Manually You may select just one of the parameter values in the main window of the Parameter Value Order box and use the move up move down buttons to arrange the order to your liking Copyright 1998 2001 Silicon Genetics 2 10 Creating DataObjects in GeneSpring Definitions of Parameters Definitions of Parameters Parameters are the variables you use to describe your experiment Parameter Vocabulary e Experiment parameters variables that can incorporate many sample parameter variables Generally speaking when the term parameter is used it means an experimental parameter As an example parameters could be e Kryptonite Concentration e Variety of Yeast e Andromeda Strain Infection e Test Repeat Number e Parameter value is one of the possible values assigned to a variable As an example the parameters values from the previous list could be e Kryptonite Concentration in ppm 0 10 20 30 40 e Variety of Yeast A or B e Andromeda Strain Infection Healthy or Infected e Test Repeat Number 1 or 2 e Sample parameters variables used to describe the precise condition under which each sam ple or measurement was taken You may have many parameter values applying to a single sample suc
242. g Drawn Genes on page 4 22 Lists Containing Your Gene In the bottom center of the Gene Inspector are the lists containing your gene Selecting one of these lists will bring up the Inspect List window For information about this window see List Inspector on page 3 44 Searching Internet Databases You can set up the Gene Inspector to search public databases To set up this search function see Genome Wizard on page C 1 Note however that the Macintosh version of GeneSpring does not allow for Gene Inspector searches of the Internet To search a database with a Macintosh go toEdit gt Preferences gt Browser and enter the appropriate pathway Notes Section In the upper left corner of the Gene Inspector under the Gene Identification Section is an area where you can make notes To save these notes click the Save Notes button Copyright 1998 2001 Silicon Genetics 3 40 Viewing Data in GeneSpring The Inspectors Experiment and Condition Inspectors Just as you can inspect a gene with the Gene Inspector you can inspect an experiment or condi tions with the Experiment or Condition Inspector To Access the Experiment or Condition Inspectors 1 Right click over the name of any experiment or condition in the navigator 2 Select the Inspector option from the pop up menu 3 Experiment Inspector E 10l x Experiment Name east cell cycle time series no 90 mi Author s Type YourNameHere Research Group Silicon Ge
243. g is catching data Click OK or wait In a moment GeneSpring will have passed all the data it needs and you will have several new folders in the navigator Each top level folder Gene Lists Experiments Gene Trees and so on will contain a new folder called GeNet containing the data just collected from GeNet The folders created in this feature are links to GeNet The data in GeNet is not really down loaded to your local hard drive as that would take up too much space If you use the Load Data from GeNet command twice in the same session you may get the folder duplicated within GeneSpring To avoid this please shut down GeneSpring between uses All items being viewed from GeNet appear in an italic font within the navigator You cannot delete a GeNet data object from the server but you can remove it from your navigator by right clicking over the data object and selecting Delete List or similar command from the pop up menu Copyright 1998 2001 Silicon Genetics 6 8 Help Contacting Silicon Genetics Technical Support Appendix A Help Contacting Silicon Genetics Technical Support You may contact Silicon Genetics Technical Services Department at 650 367 9600 or support sigenetics com There is a great deal of current useful information on the Silicon Genetics website select Help gt Frequently Asked Questions to launch your browser and reach http www sigenetics com GeneSpring faq index html The Help Menu Th
244. genome browser and select Error Bars gt Show Error Bars The error bars will be visible in the Gene Inspector as well as in the main GeneSpring window You can choose one of the following three kinds of error bars e Standard Error e Standard Deviation e Minimum Maximum Value of Each Gene To access one of these options right click on the genome browser and select the Error Bars submenu Note that to select an error bar type you must first have selected Error Bars gt Show Error Bars 3 2 Copyright 1998 2001 Silicon Genetics Viewing Data in GeneSpring Using Genome Browser Splitting Windows The Split Windows feature allows you to view several classifications or lists of genes separately in the genome browser If you switch to another view in the View menu the window will remain split While viewing split screens you can zoom pan and make changes in the experiment inter pretation the same way you do with unsplit screens s GeneSpring 4 1 Yeast Genes all genes OF x File Edit View Experiments Colorbar Tools Annotations Windows Help EJ Gene Lists Relative 5 0 _ Gene Ontolo Intensity ratio ko HH_ I PCA Yeaste ry 30 HH J PIR iaia all genes E 2 0 all asnon s 1 5 ACGCGT in r ih initially high 5 3 like Drawn G s i like YMR199 s E List with dele l Experiments 0 6 Gene Trees 5 Experiment Tret 0 4 EJ Classifications ja Bae 4x3 SOM for l5 cluster K N time minutes 0 0 EHI Pathwawe
245. gh 5 and 7 which will be normalized to the average of samples 3 and 4 In addition samples 6 8 and 9 will be normalized to sample number 5 e Example 5 The various parenthetical phrases will occur all at once so you may place any piece in any place in the string 1 2 7 7 7 3 4 8 8 8 5 6 9 9 9 is the same as 7 7 1 2 7 8 8 3 4 8 5 6 9 9 is the same as 1 2 7 7 3 4 8 8 5 6 9 9 is the same as 7 3 4 8 8 5 6 9 9 Translation samples 1 2 and 7 will be normalized to sample 7 samples 3 4 and 8 will be normalized to sample 8 and samples 5 6 and 9 will be normalized to sample 9 All values for the normalized samples 7 8 and 9 will equal one If you have a cutoff then the scaling factor for this step of the normalization is computed by tak ing the arithmetic mean over the set of control sample measurements that have values are not N A and are above the cutoff If no such values are present for a given gene then a special normal ization is done In this case the cutoff value itself is used as the basis of the normalization Any sample with a measurement level greater than or equal to the cutoff will be normalized by this fac tor and any sample with measurement level less than this cutoff will be have a normalized value set to N A This is done in order to avoid losing data for genes that might have low measurement levels in the control group but significantly upregulated levels in the treatment groups without intro
246. grabbing an edge with the cursor and dragging but it is recom mended you leave them at the large size You may not see every panel discussed here as you go through the Genome Wizard as the Genome Wizard will modify itself depending on your answers 1 SelectFile gt New Genome Installation Wizard The New Genome Installation Wizard panel will appear In this window you need to tell GeneSpring the name of the genome you are installing To name a genome a Place the cursor in the Organism Name box b Type the name of the organism as you wish it to appear in GeneSpring This name can be anything but a sensible memorable name is recommended GeneSpring will remember this name with the capitalization and the spelling you use here c Click the Next button to move forward to the next panel 2 Genome Data Directory panel will appear In this panel you can select or create a new direc tory GeneSpring will bring up a default directory named the same as the organism you just entered If you type in the name of a non existent directory GeneSpring will create it for you Later you can use the Wizard to select various files and GeneSpring will copy them into this directory automatically See Raw Data on page K 1 for the correct format of the raw data files To enter the directory a Type the complete directory pathway name in the Specify directory box If you already have a directory for the organism you named in previously GeneSpring wi
247. h a Gene Tree to a chosen directory in GeNet 1 Gene Tree input Knob for Directory No outputs 7 Groups e Merge Genes Merges a group of genes into a gene list 1 Gene Group input Output is a Gene List e Merge Genes and Numbers Merges a group of genes into a gene list with associated numbers If the genes and numbers do not match the results are undefined 1 Gene Group input amp 1 Number group input Output is a Gene List e Split Classification Splits the classification up into a group of gene lists 1 Classifica tion input Output is a Group of Gene Lists e Split Conditions Splits the Experiment interpretation into a group of Conditions 1 Experiment interpretation input Output is a Group of Conditions e Split Gene List Splits the Gene List up into a Group of Genes 1 Gene List input Out put is a Group of Genes e Split Gene List With Numbers Splits the Gene List up into a Group of Genes and an associated Group of Numbers 1 Gene List input Output is a Group of Genes amp a Group on Numbers 8 Filter e Filter Boolean Group For each Boolean in the first argument pass through the corre sponding second argument if the Boolean is true 2 Boolean Group inputs Output is a Boolean Group e Filter Condition Group For each Boolean in the first argument pass through the corre sponding Condition if the Boolean is true 1 Boolean Group input amp 1 Condition Group input Output is a Group of Con
248. h as time drug concentration etc The sample parameters are listed in the main GeneSpring navigator for every condition Please refer to Parameter Display Options on page 2 12 for more details Parameters Displayed in the Navigator ntensity ratio H Experiment WA O stage Embryonic amp stage Postnatal eE stage Adult EHS All Samples Oss stage Embryonic day 11 stage Embryonic day 13 Oss stage Embryonic day 15 stage Embryonic day 18 is stage Embryonic day 21 Les stage Postnatal day 0 as stage Postnatal day 7 LME atana Daatantal daud d an Figure 2 2 Data objects in the navigator so on nO ox mMm Copyright 1998 2001 Silicon Genetics 2 11 Creating DataObjects in GeneSpring Definitions of Parameters e Measurement The smallest unit of data used by GeneSpring you will only see measure ments as the raw values present in the upper right table in the Gene Inspector In the Graph view this will be presented as one point on one gene s line It may be easier to think of this as one spot or set of probes on one array A measurement is a number such as 7 3 If you have no replicates 1 measurement 1 raw value 1 spot on a chip e Array a set of spots on a chip typically expressed as a set of intensity measurements An array typically has one sample on it If you have gross slide problems please see Array Lay out View on page 3 22 for
249. hange any of the restrictions you defined To apply the restriction to another experiment or another condition you must begin again by right clicking over that data object in the mini navigator and selecting a new restriction Once your list is made GeneSpring will attach numbers to each gene in that list These numbers can be seen using the Ordered List view or the List Inspector Note that you can filter on any of these numbers See Adding an Associated Number Restriction on page 4 9 for details on asso ciated numbers Copyright 1998 2001 Silicon Genetics 4 9 Analyzing Data in GeneSpring Filter Genes Analysis Tools References Benjamini Y and Hochberg Y 1995 Controlling the False Discovery Rate a Practical and Powerful Approach to Multiple Testing Journal of the Royal Statistical Society B 57 289 300 Dudoit S Yang Y H Callow M J and Speed T P 2000 Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments Department of Statis tics Technical Report 578 University of California Berkeley http stat ftp berkeley edu tech reports index html Holm S 1979 A Simple Sequentially Rejective Bonferroni Test Procedure Scandinavian Journal of Statistics 6 65 70 Miller R G 1981 Simultaneous Statistical Inference Second Edition New York Springer Ver lag Westfall P H and Young S S 1993 Resampling Based Multiple Testing Examp
250. hat GeneSpring is case sensitive 5 Click the Select button to browse for the correct database Normally you will need to browser into a new computer server to access the database 6 Now there will be a new entry in the list of databases Copyright 1998 2001 Silicon Genetics Appendix E 3 Installing from a Database Connect your Database to GeneSpring Test to Make Sure Your ODBC Connection is Working 1 From Excel go to the Data menu 2 SelectGet External Data 3 SelectNew Database Query Look for your database in the presented list Connect your Database to GeneSpring A database specification file must be set up This is a plain text file in a subdirectory of the main GeneSpring data directory entitled Databases The text file should have the extension database This file will tell GeneSpring how to contact your database The file contains several lines Each line contains the name of a parameter you should set followed by a colon then fol lowed by the value you want to set the parameter to The purpose of this file is to tell GeneSpring how to read the database as if it were a simple text file It pulls the data together and places it in columns recognized by GeneSpring Column names and sample name references are entered in the Experiment Wizard as normal 1 Using your file management software create a new folder in the data directory of GeneSpring titled Databases 2 Create a file with an extension of database This
251. he Master Gene Table panel will appear You will not see this panel if you are using a Gen Bank or EMBL file for your organism Your Master Gene Table must be in a name list name function SGD or mapped format Please see What Format do these Data Need to be in on page H 1 for an example This panel tells GeneSpring what the name of your Master Gene Table is and what format it is in The Master Gene Table is referred to as a Gene List file in this panel because the list of gene names are the most important information contained in the Master Gene Table To enter the Master Gene Table s file name either type the complete path way and file name of the Master Gene Table file or a Click the Browse button A window will appear Look at the folder listed to make sure you are in the folder you want b In this new window select your Master Gene Table file for example ORF _table txt c Click the Open button This enters the filename and pathway within the Enter GeneList Filename box of the Genome Wizard The Master Gene Table file will be copied into the correct folder by GeneSpring You will not be able to go to the next panel until a Master Gene Table file has been indicated GeneSpring checks to make sure the file name you typed actually exists Beware of spelling and capitaliza tion errors because if GeneSpring cannot locate the file you indicate you will not be permitted to progress to the next panel 6 The Genome Seque
252. he function of a gene s product if known e Product tThe protein product coded for by a gene if known e Map Position A gene s mapping information e Chromosome The chromosome on which a gene is located if known e Keywords Keywords associated with a gene if known e Custom Field 1 Custom Field 2 Custom Field 3 Whatever information you may have placed here for your own use Publish to GeNet GeNet is a web database designed to distribute and visualize any organisms gene expression data from microarrays and related technologies It allows researchers to publish raw text data images annotations and the results of analyses in any file format For details about GeNet its installation and troubleshooting please refer the GeNet User s Guide You must have several different pieces of software to make GeNet work so please consult with your system administrator as needed Upload to GeNet Start GeneSpring as usual Position your cursor over a data object in the navigator you would like to upload and right click Select Publish to GeNet from the pop up menu You can publish all of the data objects present in GeneSpring to GeNet GeNet can generate magnifiable and selectable images including e bar graphs plot e classification e graph by gene e line graphs e ordered lists e pathways e physical position graphs where available e scatter plots e trees All of these types of data will be referred to as data obje
253. he image as a file on your hard drive called Picture You will need to rename this file To save the Entire Computer Screen e Windows PC Press the Print Screen key to save an image of your entire computer screen Paste the image into any program that accepts graphics and save it e Macintosh Press Shift 3 simultaneously to save an image of your entire computer screen The image will be saved as a file on your hard drive called Picture Saving Pictures and Printing You can print an image of the genome browser the genome browser with the colorbar or the dis play window Such images can be useful for reports or handouts Please use a high resolution color printer to print GeneSpring images To Print an Image of the Genome Browser and or Colorbar 1 Select the File gt Print Image command 2 Choose from the following options e Browser prints only the genome browser e Browser and Colorbar prints the genome browser and colorbar e Colorbar prints only the colorbar 3 Select a printer and click OK 6 2 Copyright 1998 2001 Silicon Genetics Exporting GeneSpring Data Exporting Gene Lists out of GeneSpring To Print an Image of the Display Window For Windows PC 1 Hold the Alt and Print Screen keys down simultaneously This will copy a picture of the active window only 2 Paste into any program that accepts graphics 3 Print For a Macintosh 1 Hold the Command Shift 4 Caps Lock keys down simulta
254. he list Classifying the Test Samples Based on the selected genes classifications are then predicted for the independent test data using the k nearest neighbors rule A sample in the independent set is classified by finding the user specified k nearest neighbors of the sample among the training set samples based on Euclidean distance between the normalized expression ratio profiles of the samples The class memberships of the neighbors are examined and the new sample is assigned to the class showing the largest relative proportion among the neighbors after adjusting for the proportion of each class in the training set Decision Threshold P values are computed for testing the likelihood of seeing at least the observed number of neigh borhood members from each class based on the proportion in the whole training set The class with the smallest p value is given as the predicted class The column labeled P value is the ratio of the p value for the best class to that of the second best class The predictor will make a predic tion if this ratio is less than the P value Cutoff specified on the initial panel and will not make a prediction if the ratio is above this cutoff Setting the p value cutoff to 1 will force the algorithm to always make a prediction but may result in more actual prediction errors Appendix O 1 Copyright 1998 2001 Silicon Genetics Technical Details for the Predictor References for the Predictor References fo
255. his error see the Gene Inspector To color your genes by significance select Colorbar gt Color by Significance Color by Static Experiment This option allows you to color your experiment by a single condition The vertical axis of the colorbar represents relative intensity on a continuous scale In the default coloration red indicates overexpression yellow indicates average expression and blue indicates underexpression The horizontal axis of the colorbar indicates the degree to which you can trust your data where dark or less intense color represents low trust and light or more intense color represents high trust for information about trust see Trust on page 3 32 To Color by Static Experiment 1 Click the sign to the left of your experiment in the navigator 2 Click the sign to the left of your experiment interpretation 3 Right click over the condition you wish to color by and select Set Static Experiment To deselect color by static experiment go to the Colorbar menu and select a different coloring scheme Color by Venn Diagram This option colors genes based on their membership in one or more gene lists in a Venn diagram For information about creating Venn diagrams and using them for analysis see Making Lists with the Venn Diagram on page 4 19 Color by Parameter This option colors genes based on the value of parameters This coloring scheme is best suited for use with Graph view and Bar Graph vie
256. his feature is only available in the Graph view when the error bars are showing Please contact Silicon Genetics technical service department at support sigenet ics com or call 650 367 9600 e Min Max This feature is only available in the Graph view when the error bars are showing Please contact Silicon Genetics technical service department at support sigenetics com or call 650 367 9600 Common Commands in the Navigator Right clicking over a list or a folder will often bring up a list of commands related to that folder Mac Users should use Control Click to activate pop up menus e Display This command will change the view to the data object selected e Inspect This command will bring up the Inspector window for the data object whether it is a list tree or something else Most of the fields in the History section of the Inspect window and for some items you will have only a History section are editable e Attachments This command allows you to view any attachment to any data object in the navigator You may also add remove or change the name of any attachment by using the Save As command Attachments can be text files pictures or anything you would like to have associated with a specific data object in GeneSpring e Delete Selecting this will result in a caution window asking you to verify the deletion of the data object Click Yes and your data object will be gone forever Some data objects cannot be deleted you should s
257. ically due to true biological activity causing the median of one chip to be much higher than another then you have masked your true expres sion values by normalizing to the median of each chip For such an experiment you may want to consider normalizing to something other than the median or you may want to instead nor malize to positive controls Region Normalization If you have more than one chip assigned to a sample and you would like to normalize them sepa rately you can do a region normalization You can also do a region normalization if you would like to normalize a region of a particular chip separately from the rest of the chip To do this you will need to load your data through the Experiment Wizard see Region Normalization on page G 15 If after loading your data you would like to change the way your regions are desig nated you can do so in the Experiment Normalizations window under Region Designators The Affine Background Correction If negative values form a large fraction of your data set GeneSpring may automatically do what is known as the affine background correction If a large percentage of your data is negative normal ization can be a problem for instance the median which GeneSpring divides your data by in Use Distribution of All Genes can be very small or even negative In such cases GeneSpring will readjust the background level for your data by adding a constant to all raw control strengths such that
258. icking the Save button If you get an error message saying your result cannot be saved rename your result and try saving again GeneSpring only checks for new scripts and loads them at startup so if you make a new script in the middle of your GeneSpring Session you will need to close and re start GeneSpring Copyright 1998 2001 Silicon Genetics 4 34 Analyzing Data in GeneSpring Creating Your own Scripts The Building Blocks of Scripts Already in your script editor are various primitive building blocks you can join together in various ways to build scripts There are several categories of building blocks 1 Boolean Boolean Generates a True or False result No inputs Knob for true or false Output is a Boolean True or false Boolean AND Output is true if and only if both inputs are true 2 Boolean inputs Out put is a Boolean Boolean False Returns the result False No inputs Output is a Boolean False Boolean NOT The Boolean output is True if and only if the input is False Converts true to false amp false to true 1 Boolean input Output is a Boolean Boolean OR Output is True if and only if either input is True 2 Boolean inputs Output is a Boolean Boolean True Returns the result True No inputs Output is a Boolean True 2 Boolean Select Select Boolean Selects 2nd Boolean input if Ist input is true and selects 3rd Boolean input if 1st is false 3 Boolean inputs Output is a Boolean
259. ight have detrimental effects Copyright 1998 2001 Silicon Genetics Appendix D 3 The Experiment Wizard The Experiment Import Wizard 2 The Data File Format panel will appear This panel tells GeneSpring where to look for your data files and what kind of format they will be in There are a number of prefabricated exper iment types a Choose one of the specific types from the drop down menu Select Fully Custom if you are unsure which of the formats offered in the What type of technology are you using box applies to you Choosing the Two color experiment File means you are using references and the panel that asks about them will already indicate you have them These prefabricated experiment types are included so you do not have to look at all of the possible wizard panels b Atthe moment Locally Accessible text files is the only selectable option for the second drop down menu c Click the Next button to proceed to the next panel 3 The Properties of Experiment panel will appear a In the top box enter the experiment name exactly as you want it to appear in the Experi ments folder in the GeneSpring navigator This name must be unique If the name is not unique GeneSpring will not allow you to move on to the next panel Enter all information carefully as GeneSpring is spelling and case sensitive b In the middle box tell GeneSpring whether you want this experiment to appear in a subdi rectory of the genome folder this ex
260. iles have a description column the Autoloader will include it in the master gene table If you have difficulties creating a genome through the Autoloader you can use the New Genome Installation Wizard see Genome Wizard on page C 1 To Create a Genome Through the Autoloader Start the autoloader 1 SelectFile gt Autoload Experiment 2 Choose the data file you wish to load 3 Verify the file format For details see The Experiment Autoloader on page 2 1 Create your genome 4 Select a genome from the Select Genome window in the autoloader If your genome is not listed enter the new genome name Click Choose Selected Genome e Ifyou have entered a new genome a second window will ask if you want to continue Click Yes Copyright 1998 2001 Silicon Genetics 2 7 Creating DataObjects in GeneSpring Change Experiment Parameters 5 You will have an option to load additional files Choose the files you wish to load GeneSpring will add genes in these data files to the genome Change Experiment Parameters You will want to use the Change Experiment Parameters window to assign parameter names and units e g time and minutes to your data For an explanation of parameters in GeneSpring see Definitions of Parameters on page 2 11 You can also use this window to add and delete parameters and rearrange the order of non numeric parameter values on the horizontal axis The Change Parameters window has an Edit menu w
261. ility between multiple sub jects in a condition between multiple physical samples for an experimental subject or patient or between multiple hybridizations of a physical sample GeneSpring can represent any one of these kinds of variability depending on the types of replicate samples you have specified in your interpretation and in the error model dialog GeneSpring assumes all replicate samples in the same condition correspond to one kind of variability The ability to estimate measurement and sample to sample variation in microarray based experi ments is often compromised by the fact that the cost in both time and materials of performing large numbers of replicate experiments is quite high If the global error model is turned on Gene Spring accounts for error instead by assuming that the amount of variability is a function of the control strength within all the measurements for a single experimental condition The advantage of making this assumption is that the number of measurements used to estimate the global error is equal to the total number of genes on any given chip Copyright 1998 2001 Silicon Genetics 2 26 Creating DataObjects in GeneSpring Global Error Models In addition measurement precision information supplied by the scanner software or indepen dently by the user can be loaded into GeneSpring via the Signal Precision column type in the column editor The value given in this column is interpreted as the standard deviation
262. iment to a single sample within the set Normalizing each gene to itself is often preferable to this normalization If you wish to normalize your data in this way select the Yes circle Another question appears Sometimes a gene s control strength in the sample being normalized to is anomalously low Enter the low est value you are willing to use for normalizations in the Enter lower reference cut off value box In the enter sample number box you can normalize multiple samples to several samples You can also normalize several samples to several samples You can normalize multiple samples to multiple different samples through a code like 1 2 3 1 2 3 4 5 3 4 which means normalize samples 1 2 and 3 to 1 and 2 and 4 5 and 6 to 3 and 4 Please see Required Syntax for Nor malization to Specific Samples on page G 10 for more information regarding the syntax to use in this panel For a mathematical illustration of this normalizing option and several examples please refer to Normalizing All Samples to Specific Samples on page G 10 The Graphics Specifications panel will appear Defining Trust The upper section of this panel tells GeneSpring what the colorbar intensity scale should be and the relative intensity values to be graphed on the y axis in the graph dis play The intensity of the colorbar in GeneSpring indicates how reliable the data for each gene is Indicate a raw very reliable a high control strength control strength val
263. imental parameters such as time or drug concentra tion Each gene is represented as a line To get to the Graph view select View gt Graph S GeneSpring 4 1 Yeast Genes like YMR199W CLN1 0 95 Fie Edit Yiew Experiments Colorbar Tools Annotations Window Ba H Gene Lists cycle tim PHO J Gene Ontology H PIR keywords 8 all genes k all genomic elem Z ACGCGT in all OF Mike YMR199W C Bo ee a D i o J u D at east cell c ycle tif Gene Trees FH Experiment Trees E Classifications HI Pathways Gt Array Layouts EH Drawn Genes E External Programs t Bookmarks 30 0v0 o xm E Scripts j time minutes 100 120 140 160 Trust time 20 0 minutes Animate Saunaan zat Zoom Out i F e i Figure 3 2 The Graph View Figure 3 2 shows the genes in the like YMR199W CLN1 0 95 list in Graph view The gene in white has been selected its name appears in the upper right hand corner of the genome browser underneath the title of the experiment Copyright 1998 2001 Silicon Genetics 3 7 Viewing Data in GeneSpring Bar Graph View Bar Graph View The Bar Graph view allows you to visualize one experiment or a set of experiments by plotting the relative expression of each gene against experimental parameters such as time or drug con centration Each gene is represented as a vertical bar To switch to Bar Graph view select View gt Bar Graph S GeneSpring 4 1 Y
264. imum number of iterations This is the maximum number of times that each centroid is recalculated after genes are reassigned to groups with the most similar centroids 6 Choose a measure of similarity For information on measures of similarity see Equations for Correlations and other Similarity Measures on page L 1 If you do not want to base the initial grouping of genes on the order of the current gene list you can choose one of these two options for selecting starting classifications e The Start From Current Classification feature groups genes according to the selected classification Note that this option is only available if you have selected a classification This option disables the Number of Clusters checkbox as it automat ically uses the number of classes in the current classification e The Test Additional Random Starting Clusters feature makes clustering as tight as possible by performing clustering several times each time starting from a dif ferent random grouping of genes and choosing the best result 7 Ifyou want to watch the k means clustering process as it occurs the Animate Display While Clustering feature shows changes in classification assignments in real time This may slow your analysis slightly 8 Click Start Clustering may take a few moments depending on how many genes are being clustered and how many iterations you chose When the clustering finishes the Choose Clas sification Name window will appear 9
265. in a Venn Diagram The submenu contains three options left right and bottom Please refer to Color by Significance on page 3 33 for more details on this topic e Color by Parameter This option allows you to color your genes by any parameter set as color code in the current interpretation Please refer to Color by Parameter on page 3 33 for more details on this topic Copyright 1998 2001 Silicon Genetics Appendix P 3 Common Commands Common Commands in the Drop Down menus Color by Classification This command allows you to color all the genes by a classification Please refer to Color by Classification on page 3 34 for more details on this topic The Tools Menu Filter Genes This command allows you to make specific lists of genes according to their expression levels or other data Please refer to the Chapter 4 Analyzing Data in GeneSpring for more details Clustering The Clustering command opens a new Cluster window In the middle of the Clus ter window is the Clustering Method drop down menu in which you can choose one of the fol lowing clustering methods e K means For more information see k Means Clustering on page 5 9 e Trees This window allows you to create new gene trees or experiment trees For more information see Trees on page 5 1 e Self Organizing Map For information on Self Organizing Maps SOM please refer to Self Organizing Maps on page 5 12 or contact Silicon Genetics tech
266. ine at zero almost all gene values are above it at 1 there are a fair amount that read below the baseline It is worth noting that for data normalized to an overall level of 1 as with all normalizations that GeneSpring performs the Pearson correlation gives you almost the same correlations as the Standard correlation when they are both performed on the logarithms of the genes expression values This is how to compute a Pearson Correlation Calculate the mean of all elements in vector a Then subtract that value from each element in a Call the resulting vector A Do the same for b to make a vector B Pearson Correlation A B A B Copyright 1998 2001 Silicon Genetics Appendix L 2 Equations for Correlations and other Similarity Measures Common Correlations Spearman Correlation The Spearman correlation is a nonparametric correlation similar to the Pearson correlation except it replaces the data for Gene A and B with the ranks of the data i e the lowest measurement for a gene becomes 1 the second lowest 2 and so forth Spearman correlation calculates the correla tion of the ranks for Genes A and B s expression data around the mean of the ranks using the same formula as Pearson correlation In the Spearman correlation only the order of the data is important not the level therefore extreme variations in expression values have less control over the correlation If there are ties in the data then all of the tied values are a
267. ine a standard group of gene lists to label the branches of a gene expression tree Improved Normalization New on the fly normalizations include more robust handling of per spot normalization normal ization of a region of a chip and normalization of SAGE data Also improved text descriptions of normalization procedures are included in the Interpretation Inspector available for every interpre tation More Advanced Regulatory Sequence Searching The Find Potential Regulatory Sequences algorithm is now speedier more flexible and allows for gaps in the putative consensus sequence Copyright 1998 2001 Silicon Genetics 1 5 Introduction New in Version 4 0 Spreadsheet Display The Spreadsheet view allows for easy tabular display of expression data for an entire gene list including e normalized signal e control signal e raw signal e t test p value e associated flags Enhanced Color Options Expanded color scheme makes visualization of up and down regulated genes easier Helpful Hints Helpful hints pop up dialog boxes will guide you through the data loading process Also new and improved Help buttons appear on many screens throughout GeneSpring Copyright 1998 2001 Silicon Genetics 1 6 Introduction GeneSpring Basics GeneSpring Basics GeneSpring is a remarkably powerful analysis tool and like any professional level program it can be intimidating to new users The following section is a brief introduction
268. ion Logarithm or Fold Change Average The mean of any normalized replicates in the experiment Minimum The minimum normalized signal values for each gene Maximum The maximum normalized signal values for each gene Standard Error tThe standard error of the normalized values for each gene Standard Deviation The standard deviation the square root of the variance of the normal ized values for each gene Raw Data Average The mean of any raw data replicates in the experiment Minimum The minimum raw data signal values for each gene Maximum The maximum raw data signal values for each gene Standard Error tThe standard error of the raw data values for each gene Standard Deviation The standard deviation the square root of the variance of the raw data values for each gene Control Value Average The mean of any control value replicates in the experiment Minimum tThe minimum control value signal values for each gene Maximum The maximum control value signal values for each gene Standard Error tThe standard error of the control values for each gene Standard Deviation The standard deviation the square root of the variance of the control values for each gene Copyright 1998 2001 Silicon Genetics 6 5 Exporting GeneSpring Data Publish to GeNet Annotations e Description A gene s description if known e Phenotype A description of a gene s phenotype if known e Function A description of t
269. ion to show what has been hidden You may need to enlarge your screen before you can see all the labels Copyright 1998 2001 Silicon Genetics 3 30 Viewing Data in GeneSpring Bookmarks Bookmarks If you ever need to pause in the midst of your analysis you can create a Bookmark to hold your place The Bookmark saves all your current display settings including experiment gene list col oration and selected genes To Create a Bookmark 1 Go to the File menu and select Save Bookmark The Save Bookmark dialog box will appear 2 Name your bookmark 3 Click Save To Access an Existing Bookmark 1 Click on the Bookmarks folder in the navigator 2 Double click over the name of any bookmark to open 1 Goto File and select Load Bookmark The Load Bookmark dialog box will appear 2 Select your bookmark 3 Click Open Changing the Coloring Scheme Color by Expression This option colors genes according to their normalized expression values and trustworthiness To color your genes by expression select Colorbar gt Color by Expression Expression The vertical axis of the colorbar represents expression levels on a continuous scale Using the default colors red indicates overexpression yellow indicates average expression and blue indi cates underexpression Genes are colored by their expression level in the selected condition as indicated by the condition line If you have specified the parameter on the horizontal
270. ioneriee snn nn a a ai aait B 4 The System Preferences sosesc aiin a E E E Gast E aca E B 5 Th Miscellaneous 555158215 scat eet icouca in a Era aeai a Ta ET ia B 5 Appendix C Genome Wizard soessosssesssecssoossoossoossssesssecssoossoosssosssoeesoossoosssossssesssoes C 1 Appendix D The Experiment Wizard sessseoesoessscsssesssecssoossoosssosssoesssoessoossoossosessose D 1 Files You will Need to Use the Experiment Wizard s sssssesssssssessseesreseesseesseseessee D 1 The Experiment Import Wizard ss ssssssesseseeseessesseeseessesstesesseesessesseeseseessresseseess D 3 Appendix E Installing from a Database oessoesssesssecssoossoosssossssesssessoossoossssssssesssoess E 1 Custom Databases and GeneSpring 2e 0sastseeieicsbandavassuadnadulstsaniasieuitesbaaiemiiaes botshatateed E 1 Databases rieres a i i a E 1 Open Database Connectivity s ssessssessssessesesssressesstssresseesresresseeseesresseeseeseesseesse E 1 Structured Query Language sssesssesssssessessessessresseesesresstesresresstessesrrssressessrssees E 2 SOL Call Cevel Interfaces rinib aE E E E HER E 2 The Genetic Analysis Technology Consortium s ssssssesseseesseessesessseesseseesseesse E 2 Databases and GeneSpring 25 25 iseacrcste cients aes eters eae ees E 3 Adding an Experiment from a Database 0 ccccesccescsssceececeseceeeseecenaeecaeceeenenes E 3 Test to Make Sure Your ODBC Connection is Working ccecseeseeteeeeeee E 4 Connect
271. iption of this window The Gene Inspector window allows you to search the associated databases to obtain more detailed information regarding a particular gene in the list e Inspecting a List in the Similar Lists Box Double clicking a list in the bottom box brings up a Gene List window displaying the genes in the selected list This window is discussed in detail in List Inspector on page 3 44 The OK button and the Cancel button at the bottom of the Inspect Gene List window both exit the Inspect Gene List window but do not close the New Gene List window Copyright 1998 2001 Silicon Genetics 4 12 Analyzing Data in GeneSpring Making Lists with the Find Similar Command Making Lists with the Find Similar Command The Find Similar command allows you to do simple correlations that is to find genes with simi lar expression profiles to the gene currently being displayed Similar genes have graphs with sim ilar shapes Each gene expression profile must have the set minimum correlation to be considered similar The higher you set the minimum correlation maximum 1 the closer the gene expression profiles have to be To Make Lists with the Find Similar command Double click on a gene this may be easier after zooming in Or 1 Select Edit gt Find Gene 2 Enter in the name of your gene 3 Press Ctrl I This will take you to the Gene Inspector Then 1 Specify the minimum correlation in the bottom left corner of the Gene Inspector
272. irectory the names will appear in the large white box at the bottom of the screen labeled Files present in the current data directory You can double click these names to insert those files into the File Name column Each row will be filled in top to bottom order each time you double click a file name until all rows are filled If your files are not shown in the Files present in the current data directory box you may not have saved your files to the correct location If you may need to recheck the Properties of the Experiment Set panel You can select from the Appendix D 6 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard list of already viewed panels on the left side of the Wizard to view that panel again If you have two files comprising a chip set you need to enter the names of both files separated by a semi colon in the same entry blank Please see You might need to put more than one file in a field To do this on page D 8 for more details Data files have the same layout when the files for each and every sample have exactly the same number of columns in the same order containing the same type of data for example signal intensity or background readings for the experiment Any variation no matter how small means your files do not have the same layout If all of your sample data is in the same file and each have the same file layout you may need to cut and paste the information into sep
273. is means that all underexpressed data appears flattened because it has to graphically fit between zero and 1 whereas overexpressed data takes up a much larger percentage of the graph from 1 to positive infinity Raw signal values that are negative which is commonly the case in Affymetrix data produce normalized values that are negative To deal with these negative values see The Affine Background Correction on page 2 23 Log of Ratio The Log of ratio mode graphs normalized values i e the ratio of the signal to the control not their logs but spaces them logarithmically The normal expression is 1 The Log of ratio interpre tation solves the problem mentioned above under Ratio where all underexpressed data appears flattened because it has to graphically fit between zero and 1 In this mode underexpressed genes take up as much space visually as overexpressed genes Logarithms of the expression ratios are used as the basis for statistical analysis Copyright 1998 2001 Silicon Genetics 2 18 Creating DataObjects in GeneSpring Changing the Experiment Interpretation Yeast cell cycle time series no 90 min Ra a 0 1 time minutes 0 10 20 30 40 50 60 70 80 100 110 120 130 140 150 160 Figure 2 4 The gene list like CLNI graphed using the log ratio formula Note that in Log interpretation the lower limit of the vertical axis is 0 01 Any expression values below 0 01 are plotted as 0 01 Note also that when yo
274. ist from Properties 2 Choose a property from the pull down menu on which to base your list 3 Deselect the Divide by semicolons checkbox if you do not want your data separated by semi colons 4 You can tell GeneSpring to include a list only if it has a certain number of members or you can include all lists By default GeneSpring removes gene lists with one or fewer members Change this number in the text box provided or include everything by deselecting the Remove classifications with 1 or fewer checkbox 5 Under Call Classification name your gene list folder 6 Click OK A new folder with the gene list you created will appear in your Gene Lists folder Making Lists with the Venn Diagram A Venn Diagram allows you to quickly visualize genes common to more than one gene list You can also find genes present in a specific list only The gray area behind the circles represents the Venn Diagram universe the selected gene list Genes in the selected list that are common to gene lists represented by the Venn diagram circles appear as numbers in those circles For infor mation about creating and filling Venn Diagrams see Color by Venn Diagram on page 3 33 To Make a list with the Venn Diagram 1 Right click the area of the Venn Diagram in which you would like to make a list Select an option from the pop up menu A New Gene List window will appear If you click in an area where two circles overlap you will have the
275. items you can show or hide The Series Variable You can change the series variable parameters such as time or drug concentration by moving the slider in the scroll bar at the bottom of the window The series variable is represented by the green ConditionLine in the genome browser Animate This command moves the series variable forward automatically To turn this feature on simply click in the Animate checkbox in the gray box at the bottom of the browser dis play or select the View gt Animate checkbox menu item If you are viewing Color By Expression the colors will change according to the expression and trust of each data point Zoom Out Button This command reverses zoom in by a factor of two in each direction There are four ways to decrease magnification One method is to click the Zoom Out button in the experiment specification area until the desired magnification is reached Another method is to use View gt Zoom Out A third method is to right click while the cursor is in the genome browser Select the Zoom Out option of the resultant pop up menu Appendix P 10 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Experiment Specifica tion area Picture To remove the picture at the bottom right of the main GeneSpring window select View gt Visible gt Picture The picture checkbox menu item should not have a checkmark after this operation is performed To display the picture go to the same menu and clic
276. ith a variety of options including the Extract Sub values feature which can conveniently automate your parameter assigning process if you set up your file names as described below To Change Experiment Parameters 1 SelectExperiment gt Change Experiment Parameters 2 Fill in the Parameter Name and Parameter Units the latter only if applicable 3 Inthe Numeric and Logarithmic rows select Yes or No from the drop down menus You can also paste data in the Sample cells 4 Click Save to change the parameters in your current experiment or Save As to save this parameter set up as a new experiment To add a parameter click the Add Parameter button To delete a parameter click the gray bar above the column you would like to delete and then click Delete Parameter To rearrange the order of non numeric parameters on the horizontal axis click Set Value Order To Sort Ascending Descending first click the gray bar at the top of the column To move individual entries click on the entry then select one of the move buttons Move Up Move Down Move To Top or Move To Bottom You also have several options under the Edit menu at the top of the window e Cut Allows you to delete entries one at a time or as a group to do the latter click on one entry and then hold the Shift key down while clicking on additional entries e Copy Allows you to copy an entry for pasting in another cell e Paste Allows you to paste a previously copied entry
277. ith the genes on the new list Some examples of associated numbers are correlation coefficients p values fold change ratios or in the case of a regulatory sequence search the number of base pairs before the promoter region Associated numbers can be found by double clicking a gene list to bring up the Gene List Inspector Restricting genes by their associated numbers is useful if you want to use this information to cre ate a more specific list of genes For example you may want to find genes that are highly similar to another gene with a high correlation coefficient or genes that are a specific distance from a promoter found using the Find Potential Regulatory Sequences tool Adding an Associated Number Restriction 1 Right click the list with associated numbers in the Filter Genes window navigator This can also be accessed in complex correlations or clustering 2 SelectAdd Associated Numbers Restriction You will see a new Associated Numbers Restrictions window 3 Enter minimum and maximum restriction values in the fields provided and click OK The option is disabled if you right click a gene list with no associated numbers For example this restriction cannot be applied to the all genes or all genomic elements lists because there are no associated values Changing a Restriction When you double click a restriction GeneSpring will bring up a dialog box with the current restriction information From there you can c
278. ive signal levels may still be present for a few measurements GeneSpring offers the option as the last step of normalization to set these val ues to zero Also when interpreting data in logarithm or fold interpretations GeneSpring treats all normalized ratio values less than 0 01 including 0 and negative values as if they had a ratio of 0 01 preventing transformation problems Normalization for Particular Array Types For Affymetrix or One color experiments you should normalize each sample to itself as described in Normalize Each Sample to Itself on page 6 and normalize to a single sample as described in Normalizing All Samples to Specific Samples on page 10 Or you can normalize each gene to itself as described in Normalizing Each Gene to Itself on page 8 For Two color experiments normalize each gene to reference as described in Normalize to Control Channel Values for Each Gene on page 3 Then normalize each sample to itself as described in Normalize Each Sample to Itself on page 6 that is not done by your scanner soft ware Appendix G 18 Copyright 1998 2001 Silicon Genetics Creating Folders for New Genomes Raw Data Appendix H Creating Folders for New Genomes Normally GeneSpring will create new folders for you when you use the Genome Wizard See Genome Wizard on page C 1 for more details To manually create a new folder in the genome browser you must go through a file manag
279. ize to Positive Controls vs scissisccgcstesecodtisertsevasneecliccnsciaa Mapas eas nasiaevidescnzins G 5 Mathematical Illustration the Normalize to Positive Controls Method G 5 Normalize Bach Sample to Itself 2c ccccvnveccoec esigeeavad ob cedaseos dents cynactsvsentedeenedemnieomiaes G 6 Mathematical Illustration of the Normalize Each Sample to Itself Method G 6 Normalizing Each Sample to a Hard Number 0 cccccccesccesceesceeeeeeeeeeeeeeseeeeneees G 7 Normalizing Bach Gene to Itself s a icascuniceesuiiee aes sane cuaeeaySs G 8 Mathematical Illustration of the Normalizing Each Gene to Itself Method G 8 Normalizing All Samples to Specific Samples 20 0 0 ccccecceeceeeseeesseeeteceeeeeeeeeeaees G 10 Required Syntax for Normalization to Specific Samples 0 0 0 0 eeeeeeeeereees G 10 Mathematical Illustration of the Normalizing Samples to a Specific Sample Method s sscs5 oir b asics eect aa cat uae a euee neater oad G 12 Regi n Normalization speissi aaan aara AT a a SRA TAa EAA AR ioi G 15 Dealing with Repeated Measurements ss nnssssessesessseessessesseessessrssresseeseesrrsseeseesess G 16 Single Data Fle serieak i a a a aa tte E EA aS G 16 Mathematical Illustration of the Dealing with Repeated Measurements in a Single Data File Method sssssessseseesessesessseseesesseseese G 16 Meas rement Flag Sancin a a S EE ERES G 17 Negative Control Strengths ss sessessssessesessseessesersssessessesstessessessresse
280. k in the Picture checkbox menu item leaving a check in the checkbox menu item Sec ondary Picture The secondary picture will be shown in the very bottom right corner of the GeneSpring Window Secondary Animation Controls The secondary animation controls are underneath the pri mary and behave in the same manner Magnification To hide the numerical magnification value and the Zoom Out button which appears in the bottom gray box of the browser display select the View gt Visible gt Magnification checkbox menu item to deselect The magnification checkbox menu item should not have a checkmark after this operation is performed To display the numerical mag nification value and the Zoom Out button at the bottom of the browser display go to the same menu and select the Magnification checkbox leaving a check in the checkbox menu item This does not disable the zoom functions which can still be done through other menus See the Zoom In Zoom Out and Zoom Fully Out commands above for a description of these functions and directions for how to employ them Appendix P 11 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Experiment Specifica tion area Appendix P 12 Copyright 1998 2001 Silicon Genetics Glossary Appendix Q Glossary A Array a set of spots on a chip typically expressed as a set of intensity measurements An array generally has one sample If all of the interesting genes fit onto one array th
281. l appear Name your list and click Save For more information about this window see New Gene List window on page 4 11 Creating Drawn Genes The Creating Drawn Genes function allows you to draw a pseudo gene to represent a hypothetical expression pattern This function is useful if you have some idea of what gene expression pattern you are looking for as you can simply draw a pattern and look for genes that behave similarly You must be in Graph Bar Graph Scatter Plot or Graph by Genes view to create a drawn gene Double clicking on the drawn gene will open the Gene Inspector for that gene To create a drawn gene 1 Select Tools gt Show Drawable Gene A new gene will appear on the screen at the normalized median of your data usually 1 0 2 To change the shape of this gene click on the gene and drag it while holding down the control key e Mac Users Please use Option Click to alter your Drawn Gene To save a Drawn Gene 1 Double click the drawn gene to open the Gene Inspector 2 Click the Save As Drawn Gene box in the bottom left of the window 3 Give your new profile a name and click Save Your new drawn gene will appear in the Drawn Genes folder in the navigator Copyright 1998 2001 Silicon Genetics 4 22 Analyzing Data in GeneSpring Pathways To make Lists from Drawn Genes 1 Double click the drawn gene to open the Gene Inspector 2 Click the Find Similar button in the bottom left corner of the window A New
282. l in the current condition and the horizontal position represents its control strength in this case the median expression level of this gene in all conditions Thus Copyright 1998 2001 Silicon Genetics 3 15 Viewing Data in GeneSpring Scatter Plot View genes that fall above the diagonal are overexpressed and genes that fall below the diagonal are underexpressed as compared to their median expression level over the course of the experiment To view a Scatter Plot l 2 Select the View gt Scatter Plot option From the navigator panel right click the sample condition or gene list that you would like represented on the vertical axis and select the Use on Scatter Plot gt Vertical Axis option from the drop down menu From the navigator panel right click the sample condition or gene list that you would like represented on the horizontal axis and select the Use on Scatter Plot gt Horizon tal Axis option from the drop down menu Right click the horizontal axis and select the Horizontal Axis Mode option Select one of the following data types from the submenu that appears e Relative normalized to display the normalized expression value as defined in the cur rent experiment this is the most common option e Control to display the control signal as defined in the current experiment See Per chip Normalizations on page 2 22 e Raw Signal to display the raw signal without normalizations applied e Average of Raw
283. l missing from your experiment is e the default normalizations e specifications for Display Options e specifications for Table Headings Entering your Prepared Database into GeneSpring Using the Experiment Wizard select the Get Everything from the Database option The majority of the remaining Experiment Wizard panels will be filled in automatically If you left the debug setting for true an extra window will open up When the query boxes come put these will contain actual SQL commands GeneSpring will have to go back to the database to get information every time you restart the pro gram If this takes too long you might consider right clicking over the correct database icon and selecting the save to disk option All commands in the experiment files can also be added to the database file Appendix E 5 Copyright 1998 2001 Silicon Genetics Installing from a Database Entering more Complicated Data from a Database Entering more Complicated Data from a Database You can link various tables together in SQL This typically requires a proficient user of databases please check with the person who built your database if you have questions There are many ways to enter and organize data within databases If the data organization in your debase if confusing you might want to make separated tables for your data or part of your data For example you could make a separate table just for parameters like Table B 2 Sample 1 Param
284. lassifications Pathways Array Layouts Drawn Genes External Programs Bookmarks Scripts Copyright 1998 2001 Silicon Genetics Tools Annotations Window Help day 11 Embryonic Magnification 1 so on nO o x mMm 0 Trust Animate Zoom Out JE Figure 5 3 PCA in the Ordered List view Clustering and Characterizing Data in GeneSpring Principal Components Analysis References for Principal Components Analysis Alter O Brown P O Botstein D Singular value decomposition for genome wide expression data processing and modeling PNAS 97 10101 6 2000 http www pnas org cgi content full 97 18 10101 Cooley W W and Lohnes P R Multivariate Data Analysis John Wiley amp Sons Inc New York 1971 Gnanadesikan R Methods for Statistical Data Analysis of Multivariate Observations John Wiley amp Sons Inc New York 1977 Neal S Holter et al Fundamental patterns underlying gene expression profiles Simplicity from complexity PNAS 97 8409 2000 http www pnas org cgi content abstract 97 15 8409 Hotelling H Analysis of a Complex of Statistical Variables into Principal Components Journal of Educational Psychology 24 417 441 498 520 1933 Kshirsagar A M Multivariate Analysis Marcel Dekker Inc New York 1972 Mardia K V Kent J T and Bibby J M Multivariate Analysis Academic Press London 1979 Morrison D F Multivariate Statistical Methods Second E
285. ld be put in the same folder within GeneSpring s data directory The default data directory for GeneSpring in a PC is C Program Files Silicon Genetics GeneSpring data In this data directory use your file management program to create a new sub directory to hold the new genome data This folder is usually named after the organism you are adding but any memorable name will suffice There are three possible raw data files you may have when you create a new genome 1 You must have a Master Gene Table or a GenBank EMBL file s 2 You can have sequence data in seq format 3 You may have a file containing extra non GenBank genes if you have any The file of extra genes should be in one of the four standard Master Gene Table formats The three raw data files should all be placed within your new subdirectory Appendix H 6 Copyright 1998 2001 Silicon Genetics Installing a Genome from a Text File Creating Folders for New Genomes Appendix Installing a Genome from a Text File The following steps are needed to load a genome These steps are essentially the same as the ques tions you answer in the Genome Wizard The specific examples and instructions given are for E coli 1 Open the GeneSpring data directory typically C Program Files SiliconGenetics Gene Spring data using your file management program 2 Create a sub directory to hold the new genome data 3 Copy your Master Gene Table GenBank or EMBL file s in this new direct
286. ld displays information such as the type of clustering the distance metric and the number of iterations that were used to perform the clus tering You can save your own comments about the classification here for future reference The bottom half of the Classification Inspector contains a table with three columns e Class the name given to each class e Genes the number of genes in each class e Average Radius the root mean square of the Euclidean distances between each gene and the centroid of each class Classes with large radii are spread out and classes with small radii are tightly grouped Copyright 1998 2001 Silicon Genetics 3 46 Viewing Data in GeneSpring The Inspectors At the bottom of the Classification Inspector window the Percent Explained variability is dis played This number is a measure of the quality of the classification classifications in which the average radius of each class is small and in which the centroids of each class are located far apart from one another explain a high percentage of the total variability GeneSpring expresses the per cent explained variability E as E 100 G 1 G Where G is the Calinski and Harabasz index of quality G B c 1 W n c B is the sum of the squares of the distances between the cluster centroids and the mean of all genes in all classes W is the sum of the squares of the distance between all genes and the centroid of the class to which the gene belongs n is the
287. lect Use as Classi fication 3 Right click over that folder again and select Split Window gt Both Color by Secondary Experiment The Graph and Scatter Plot displays lend themselves to being colored in many different ways because the display presents expression levels of the genes through the entire experiment These are the only views in which you may choose to color the genes by a secondary experiment This means the color of each gene line graphed correlates to the expression level of that gene in a dif ferent experiment at the point in the second experiment marked by the secondary scroll bar Copyright 1998 2001 Silicon Genetics 3 35 Viewing Data in GeneSpring Changing the Coloring Scheme g 1 From the navigator open the Experiments folder by clicking on its icon 2 Position your cursor over an experiment not the one currently displayed you would like to use for coloration 3 Right click and select Set Secondary Experiment from the pop up menu The coloring scheme of the genome browser will be shown in the colorbar on the right There will be two versions of the animation controls in the Experiment Specification Area Changing the Experimental Data Range Before you change the experimental data range you will need to select Colorbar gt Color by Expression 1 Right click over the colorbar and select Set Range from the pop up menu 2 Reset the values determining the intensity of the colors used by the genome browser
288. les and Meth ods for p Value Adjustments New York John Wiley amp Sons Inc Copyright 1998 2001 Silicon Genetics 4 10 Analyzing Data in GeneSpring Filter Genes Analysis Tools New Gene List window The New Gene List window is created when a new list is made It allows you to accept or reject the list after seeing the genes it contains and it allows you to set the name of the list The example in Figure 4 1 is the result of doing a correlation to find all genes with a similar expression profile to YMR199W CLNI1 S New Gene List 117 genes Genes with correlation of at least 0 95 to YMR199VV CLN1 in experiment Yeast cell cycle time series no 90 min Notes Gene Lists YMR1 SSW G sub 1 cyclin YKLO42VV component of the spindle pole body YDR4507C putative serinevthreonine kinase YJL181vV YMLO21C uracil DNA glycosylase YEROSS5VY RecA homolog Rad51p colocalizes to 65 spots with Dmeip YCRO6S5V Transcription factor fork head domain YMR305C soluble cell wall protein x Similar lists P value List Name 4 1055124E 8 cell cycle 1 0463E 7 DNA replication and chromosome cycle 8 309066E 6 DNA metabolism 4 8828224E 4 DNA repair 0 0016408522 chromosome 0 0020823039 DNA strand elongation 0 0020823039 DNA replication Save Cancel Figure 4 1 The New Gene List window This list was a result of searching for all of the genes in the Yeast cell time series no 90 min experime
289. listed here and in the Experiment Nor malizations window To get to the Experiment Normalizations window to assign normalizations select Experiments gt Experiment Normalizations Background Subtraction To estimate background noise some chips come with negative control spots that do not corre spond to mRNA from the species under study Even if your imaging software automatically sub tracts background fluorescence you may still want to tell GeneSpring to normalize to negative controls The formula used here is signal strength of gene A in sample X median signal of the negative controls in sample X To Subtract Background Noise 1 Create a negative control file by listing the names of your negative controls in the first column of a spreadsheet file and saving in tab delimited text format 2 Click the Use negative controls box 3 Browse for the name of your negative control file Copyright 1998 2001 Silicon Genetics 2 21 Creating DataObjects in GeneSpring Per chip Normalizations Per spot Normalization If you are conducting a two color experiment you will probably want to do a per spot normaliza tion The formula for this normalization is signal strength of gene A in sample X control channel value for gene A in sample X To Perform a Per spot Normalization 1 Under Per spot normalizations choose either Use control channel to calculate ratioorUse control channel for trust depending on whether or not your
290. ll ask you to define a subdirectory If you are starting a new species directory this will be unnec essary Or if you have already created a directory as specified in Creating Folders for New Genomes on page H 1 you will need to type in or browse to find that directory To browser to a directory a Click the Browse button A dialog box will come up showing the data folder in Gene Spring Before you begin browsing look at the folder to make sure you are in the folder you want b Find the file directory folder containing your raw data files Appendix C 1 Copyright 1998 2001 Silicon Genetics Genome Wizard c Click the directory file folder This opens the directory You should see your raw data files within this directory d Click the Save button This writes the pathway in the Specify directory box of the Genome Wizard When you click the Save button in the Browse directory window the File Name box in the window contains the file name Dummy Name leave alone This is what the window is supposed to look like when you click the Save button If you accidentally click one of the files within the genome s directory the name in the File name box changes Then when you click the Save button you will get an error message Click the Yes button of this error message this does not replace the raw data file it simply enters the directory of the correct file into the Specify directory box of the Genome Wizard Click
291. ll appear In this panel you tell GeneSpring if you have any pictures of the array plates used Microarray pictures are nice but not necessary If you don t have any leave the No circle selected and proceed to the next panel To associate Array Pictures with the samples select the Yes circle for the question Do you have any pictures of the microar ray plate s A table appears In the GIF File Name column enter the complete name of the file containing the array picture to be specifically associated with the sample listed in the left hand column If you have an array picture for every sample GeneSpring will display it when you double click the picture in the lower right hand corner of the main GeneSpring window Array pictures must be in either GIF or JPEG format When you right click the table in this panel of the Experiment Wizard there are pop up menus allowing you to cut and paste You can also cut and paste entries into the matrix fields by using the keyboard commands for Windows this is Ctrl C and Ctrl V The pop up menu resulting from right clicking the GIF File Name label allows you to copy and paste columns The pop up menu resulting from right clicking the experiment labels section of the table allows you to copy and paste rows The pop up menu resulting from right clicking the gray field in the upper left hand corner of the table allows you to copy and paste all These pop up menus allow you to cut and paste large sections of th
292. ls GeneSpring how many parame ters were used in this experiment and what those parameters were Briefly a parameter is anything used to describe the condition or conditions of the experiment A parameter consists of two or more parameter values for example breast cancer lung cancer and healthy could be parameter values for the parameter cancer For a more detailed description of parameters see Definitions of Parameters on page 2 11 a Type the number of parameters involved in this experiment in the Number of parameters box Changing the number in this box changes the number of lines given in the table below b Name each of your parameters in the right hand column labeled Parameter Name You can tab forward or use the cursor keys in some cases to place the cursor in the next space When you right click this table there is no pop up menu allowing you to cut and paste You can still cut and paste entries into the matrix fields by using the keyboard commands for win dows this is Ctrl C and Ctrl V If you right click one of the gray areas of this table a pop up menu will appear These pop up menus allow you to cut and paste large sections of the table You cannot pro ceed to the next panel until you have named all of your parameters If you mis typed the num ber of parameter values just highlight over it and type in the correct number c Select the Next button to continue 6 The Parameter Characteristics panel will appe
293. luding the types of files necessary Clicking the Help Pasting Data button will take you to a web page with information on pasting experiments directly into GeneSpring Pasting is very easy if your file is set up cor rectly but it is not very flexible Please refer to Copying and Pasting Experiments on page F 1 for more information The Experiment Wizard is very flexible and correspondingly more complex The Welcome panel includes lists to remind you to create or gather your raw data files There are five possible raw data files listed below only the first one is necessary for loading an experiment They should all be placed within the Experiment sub folder of the relevant organisms described in Where do I put my data on page K 8 e Experimental data file s containing the genes control strengths for each sample in the experiment e A file listing the positive controls e A file listing the negative controls e GIF or JPEG pictures to be associated with this experiment or with particular sam ples within the experiment e GIF or JPEG pictures of the Microarray plates the experiment was done on Click the Next button to proceed to the next panel As you move to the next panel a check box in the Wizard navigator will change color You can return to any of the previous panels by clicking the check box of the panel you would like to view again Occasionally you will get a dialog box telling you changes in a previous panel m
294. lumn to indicate the experiment worked Often this is just a letter such as P for Present or Passed If you do not have an experiment worked column skip this question and the associated experiment entry StatusOkString the value letter or word indi cating the sample is ok to use StatusOkString P You can have more than one entry indicating the status If you were not sure if your experi ment recorded P for passed or O for OK place both in the line separated by vertical bars You might also have a designation for Marginal or Questionable data Often this is just a let ter such as M for Marginal StatusMarginalString the value letter or word indicating the sample is of marginal quality StatusMarginalString M Q You might also have a designation for Failed or Absent data Often this is just a letter such as A for Absent StatusFailedString the value letter or word indicating the sample is absent StatusFailedString FIA Associating a Picture with a Sample Pictures are nice but they are not necessary If you don t have any skip this section and the asso ciated experiment file entries 23 If you have any pictures you wish to associate with any or all of the samples use the line given below to tell GeneSpring where to find the picture If you do not have a picture to associate with every sample GeneSpring will display the picture associated with the next closest sam ple with an associated pictur
295. ly it is because of a spelling or capitalization error Due to the complexity of the information contained in the experiment file this section is designed to help you create a experiment file for a particular experiment rather than explaining exactly what each possible answer means There are two examples following each question The first is the generalized form of the answer including the generalized object name and what sort of response constitutes a correct object value The second bold faced example is an example of an actual answer to the question A fictitious experiment Yeast extraterrestrial studies is used as the example experiment throughout this chapter A complete experiment file for the Yeast extra terrestrial studies experiment is given in this chapter There are eighteen sections and thirty eight questions which must be answered in their presented order Define Your Experiment 1 Enter the name of your experiment or samples as you wish it to appear in the GeneSpring menu system name Your experiment name here name Yeast extraterrestrial studies 2 How many samples are there in the experiment you have just named A sample is defined as each time a numerical measurement is taken for your entire set of genes Experiments The number of samples Experiments 40 Appendix J 1 Copyright 1998 2001 Silicon Genetics Installing from a Text File Define Your Parameters 3 How many different parameters wer
296. m directory In a Windows NT environment your path may look something like this C Program Files Plus Microsoft Internet IEXPLORE EXE e Find and select the exe file associated with your internet browser e Click the Open button in the Browse window This writes the complete exe file name and pathway in to the Browser path box of the Preferences window d Click OK to close the Preferences window The path to your browser should be set 9 The Miscellaneous Settings panel will appear This panel lets you alter the way the gene names are displayed a Ifyou wish to force all of the systematic gene names to upper or lower case letters select the appropriate check box It is perfectly acceptable not to select any of the check box options b Select Next to proceed to the next panel 10 The Finished panel will appear When you click the Finish button all of the answers you gave in the previous Genome Wizard panels are saved in a genomedef file Appendix C 5 Copyright 1998 2001 Silicon Genetics Genome Wizard Appendix C 6 Copyright 1998 2001 Silicon Genetics The Experiment Wizard Files You will Need to Use the Experiment Wizard Appendix D The Experiment Wizard Before you begin installing your new experiment you need to go through the Genome Installation Wizard to specify a new genome if the genome for your experiment is not yet in GeneSpring so GeneSpring will correctly interpret what you are telling it If you are
297. malization you will want to use constant values For instance Affymetrix s Global Scaling centers your data around 2500 in this case you would need to normalize your data to 2500 to center it around 1 signal strength of gene A in sample X hard number in sample X To use Constant Values 1 Under Per chip normaiizations click Use constant values 2 Specify the hard number for each of your samples Copyright 1998 2001 Silicon Genetics 2 24 Creating DataObjects in GeneSpring Per gene Normalizations Per gene Normalizations Normalize to Median For Each Gene This per gene normalization accounts for the difference in detection efficiency between spots It also allows you to compare the relative change in gene expression levels as well as display these levels in a similar scale on the same graph GeneSpring uses the following formula to normalize to the median for each gene signal strength of gene A in sample X median of every measurement taken for gene A throughout your experiment To Normalize to the Median For Each Gene 1 Under Per gene normalizations click Use median for each gene 2 Enter a number that is an estimate of the lowest signal value that you trust If a median value falls below this cut off the program will instead divide by the cut off GeneSpring will not allow you to do this normalization and normalize to sample s as they address the same issue Normalizing to Sample s In normalize to sam
298. malizations Each Gene to Itself 34 Do you want to normalize each gene to itself so the median of all of the measurements taken 35 for the gene is one See Normalizing Options on page G 1 for more information about this option If you are not doing a two color experiment you generally want to do this NormalizeEachGen ither true or false NormalizeEachGene true Skip this question and the associated entry if you are not normalizing each gene to itself Sometimes something will go wrong with the samples and all of the values for a particular gene are very low in which case GeneSpring will artificially inflate the noise of the gene if you normalize those values up to a median of one To specify where this cut off is type the line below in the experiment file NormalizeMinMedian the numerical cut off value below which you will not normalize a gene to itself NormalizeMinMedian 0 01 The number indicated in the example 0 01 is the default cut off value If you do not enter this line this is the cutoff value GeneSpring will use Normalizations Each Sample to a Specific Sample 36 Do you want to normalize each sample to one sample within the experiment If so enter the number of the sample counting from zero as the object value in the line below Silicon Genet ics does not recommend suggest using this normalization option unless you have very spe cific reasons as described in Normalizing Options on
299. measurements in a single data file are assumed to be repeats and will be averaged before any of the six main normalizations are imple mented See Dealing with Repeated Measurements on page 16 for details Appendix G 1 Copyright 1998 2001 Silicon Genetics Normalizing Options Background Subtractions Background Subtractions When considering how to transform raw data to normalized data the first thing that may be neces sary is to subtract an estimate of background level The background level is taken from a separate column in your data set Typically there will be a column labeled negative control containing information on the background level data The median value of the negative controls will be sub tracted from the raw values for each gene before anything else is done Normalize to Negative Controls If you have any genes designated as negative controls on your array usually you have negative controls when there is DNA from a different genome than the one you are investigating on the array you can normalize the data using this information This normalization removes the back ground from the experimental readings by giving you a general idea of the lowest amount of exposure possible for signals taken from a particular array and then subtracting this amount from your raw experimental results The formula used is the control strength of gene A in sample X the median signal of the negative controls in sample X Once you norm
300. med VAR1 in which case you should rename VARI to something else in your application Once you have all three files set up restart GeneSpring and open the External Programs folder There should be an entry named FASTCLUS If you select this item you will see SAS put up a batch window while it is running then GeneSpring will come back with a classification based on the SAS clustering and you can save and work with the classification in GeneSpring Copyright 1998 2001 Silicon Genetics 4 43 Analyzing Data in GeneSpring External Programs Example File Access The File Access external programs are a set of Java programs written using the GeneSpring Exter nal Program Interface that allow you to read and write GeneSpring data objects to and from files These functions are Load Classification From File Load Experiment From File Load Gene List From File Load Gene List With Numbers From File Load Tree From File Save Classification To File Save Experiment To File Save Gene List To File Save Gene List With Numbers To File Save Tree To File These correspond to the data formats previously discussed Experiment here means Experiment Data with Confidence These provide convenient alternatives to using the clipboard to copy and paste data from GeneSpring To use the Save features select the object you wish to export and then click on the corresponding Save command A file naming dialog will appear
301. ment about the number expected will simply say expected number of genes by chance is 100a of the genes identified This procedure provides a good balance between discovery of significant genes and protection against false positives since occurrence of the latter is held to a small proportion of the list and will probably be the best choice of multiple testing correction for most situations Copyright 1998 2001 Silicon Genetics 4 6 Analyzing Data in GeneSpring Filter Genes Analysis Tools Restrictions over a Single Condition or Sample Expression Restriction The Expression Restriction finds genes with expression values that fall between specified mini mum and maximum values for a particular condition This tool is useful if you want to find genes that respond similarly to a given condition For example you may want to find genes in an inhib itor treated sample with a minimum normalized expression of 3 For details on the types of data you can apply this restriction to please refer to Data Types for Restrictions on page 4 7 Condition to Condition Comparison Restriction The Condition to Condition Comparison Restriction finds genes based on a comparison between two samples or conditions This tool is used to find fold changes in gene expression levels between two samples or conditions 1 Select an individual sample or condition 2 Right click the sample or condition and select Add Condition to Condition Com parison Restriction
302. mic elements gene list You can change the default genome that GeneSpring initially opens by going to Edit gt Preferences selecting Data Files from the pull down menu and typing a genome name in the Default Genome text field The GeneSpring Hierarchy of Objects or Where Is My Data Stored Understanding the GeneSpring file structure can be helpful for installing updating and working with GeneSpring In your Programs folder Windows or Applications folder Mac OS you will find the Silicon Genetics directory containing GeneSpring and jre Copyright 1998 2001 Silicon Genetics 1 15 Introduction GeneSpring Basics The GeneSpring folder contains bin data docs and UninstallerData folders The principal Gene Spring program file GeneSpring jar is kept in the bin folder License keys belong in the data folder and documentation is stored in the docs folder eB SiliconGenetics E GeneSpring bin data Human Academic Chips A Human JeExperimentTrees Experiments GeneLists ExperimentTrees Homology Tables GeneLists Homology Tables lead oo E Human genomedef o Rat 2 OncoGenes txt yeast Choc_Mouse Commercial Chips e coli Experiment Formats _ GeNet H I HUSA Programs 2 Script docs UninstallerData jre UninstallerData F F Figure 1 5 GeneSpring 5 internal data structure The data folder is also impo
303. more information If all of the interesting genes of the genome fit onto one array then the terms array chip and sample can be considered synonymous e Sample The data generated from a biological object placed onto an array or set of arrays A sample s data is visible in the GeneSpring navigator under the All Samples icon e Condition A unique combination of parameters as applied to your sample Each condition may be a single sample or a group of replicate samples combined based upon the parameter values defined for each sample The easiest way to think of this is as the parameters under which the sample s was observed If you have no replicates condition and sample can be considered synonymous In Figure 2 2 the conditions are Embryonic Postnatal and Adult e Interpretation A description of how GeneSpring displays the data for you to view It would include a definition of applicable parameters and how the normalized numbers should be treated This is the way a set of conditions is grouped In Figure 2 2 the interpretation is the Default Interpretation e Experiment a set of samples generally designed to answer specific types of questions The data are usually but not always manipulated in a normalized form In Figure 2 2 the experi ment is the Rat Study A Note on Multiple Parameters The more experimental parameters you have the more options you have for visually querying your data If you have samples of tissues infected with
304. n The horizontal label is displayed in the bottom right corner of the Physical Position view To hide this label right click while the cursor is in the genome browser A menu will appear go to the Options submenu and select the Hide Horizontal Label option To show this label go to the same menu and select the Show Horizontal Label e Show Vertical Label Hide Vertical Label This feature allows the vertical label which runs along the left side of the graph to be seen or hidden Normally in the Graph view the vertical label is Expression To hide this label right click while the cursor is in the genome browser A menu will appear go to the Options submenu and click the Hide Vertical Label option To show the vertical label go to the same menu and click Show Vertical Label e Label vertical axis on side Label vertical axis at top This feature is only applicable if the vertical axis label is visible The label may appear either at the upper left hand corner of the graph or along the side next to the vertical axis To label along the side right click while the cursor is in the genome browser window A menu will appear Go to the Options submenu and click the Label vertical axis on side option To label at the top go to the same menu and choose Label vertical axis at top e Hide Experiment Name Show Experiment Name You can show or hide the experiment name look for it in the upper right corner of the Genome browser by right clicking in
305. n number of the column containing the control channel values ReferenceColumn 9 20 If your data includes the control channel s background signal which column of your data file contains that information If your data does not have control channel values skip this ques tion and the associated experiment file entry Experiment ReferenceBackColumn number of the column containing the control channel s background signals for the sample indicated Experiment1ReferenceBackColumn 7 Experiment2ReferenceBackColumn 12 Experiment 3ReferenceBackColumn 17 Experiment 4ReferenceBackColumn 22 Experiment5ReferenceBackColumn 27 Experiment 6ReferenceBackColumn 32 Experiment 7ReferenceBackColumn 37 Appendix J 11 Copyright 1998 2001 Silicon Genetics Installing from a Text File Measurement Flags If your data is all in the same file you will have to indicate the control channel background col umn for each experiment illustrated above This is also true if you have two or more data files with different columns containing the control channel s background values If on the other hand you have separate data files with the same column containing the control channel s background values you may use the general object name given below rather than entering the column num ber of the control channel s background values for each file ReferenceBackColumn number of the column con taining the control channel s background val
306. n genes represented graphically in the genome browser and on gene names found in lists Tip To select a gene in the genome browser first zoom in on it To Select Multiple Genes e Click once on any line or square representing a gene Hold down Shift to add more genes Clicking a selected gene while holding Shift deselects that particular gene Or e Shift and drag your mouse across genes you would like to select You will see a box appear as you drag When you release the mouse the selected genes will be highlighted When several genes are selected no gene names appear in the genome browser If some selected genes do not appear in the current view the upper right corner of the genome browser will display the message Some selected genes not shown Click anywhere in the browser to deselect genes List Inspector Right clicking over a list icon in the navigator will bring up several options including Inspec tor Selecting the Inspector command will open a List Inspector window displaying the common and systematic names of all the genes in the gene list currently being displayed in the genome browser You can select one of the listed genes by double clicking for closer inspection For more information on this window see List Inspector on page 3 44 Copyright 1998 2001 Silicon Genetics 3 5 Viewing Data in GeneSpring Showing Hiding Window Display Elements g Showing Hiding Window Display Elements You have the
307. n the table given in the Parameter Values panel each parameter you named has its own column Appendix D 5 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard a You must fill in every field in each column with the appropriate parameter value for the samples named to the far left of the field If there are more fields than fit in the panel scroll bars will appear You can cut and paste entries into the matrix fields by using the keyboard commands for windows this is Ctrl C and Ctrl V Pasting is highly recommend because the parameter value entries are spelling and case sensitive If you right click one of the gray areas of this table a pop up menu will appear The pop up menu resulting from right clicking the parameter labels section of the table will say copy and paste columns The pop up menu resulting from right clicking the sample labels section of the table will say copy and paste rows The pop up menu resulting from right click ing the gray field in the upper left hand corner of the table will say copy and paste all These pop up menus allow you to cut and paste large sections of the table Once you have filled in every field in the table you can proceed to the next panel by clicking on the Next button If there is an unfilled box the Next button will remain disabled b Select the Next button to continue 9 The Describe your Data Files panels will appear This panel tells GeneSpring where to find
308. nce File panel will appear You will not see this panel unless you indicate in the Overall Genome Properties panel that your genome has been sequenced and you are not using a GenBank or EMBL file This panel tells GeneSpring where to find the sequence data To do this click the Enter Genome Sequence File Name box and type the complete file name and pathway or a Click the Browse button A window will appear Look at the listed folder to make sure you are in the folder you want b Select the seq file containing your organism s sequence c Click the Open button This enters the file name and pathway into the Enter Genome Sequence File Name box of the Genome Wizard You cannot go onto the next panel until you have entered a file name The sequence data file will be copied by GeneSpring to the correct directory The file you indicate in the Enter Genome Sequence File Name box must exist or the Genome Wizard will not let you continue Copyright 1998 2001 Silicon Genetics Appendix C 3 Genome Wizard Beware of spelling and capitalization errors as GeneSpring needs to locate the file before allowing you to progress to the next panel 7 The Additional Genetic Elements panel will appear This table tells GeneSpring if you have a second table of genes Generally a second table of genes is used if you want to add genetic ele ments to a GenBank or EMBL defined organism In this case the supplementary table of genes probably contains alleles c
309. ncer j 7n 7 n gt n gt n nas 7 gt 7 gt FY 7A FY gt FY 7 gt y gt y gt y gt ys gt y gt y gt y gt y gt y gt FY gt gt y y gt y gt y gt Type Cancer 7 n 7 n s n n gt n braintbreast kidn gt livertbraintbreast kidney gt livertbraintbreast kidney gt livertbrainthbreast kidney gt livertbraintbreast 7 kidney gt livertbraintbreast kidney gt liver YaLOO1ICcC gt 0 941666722 40 575000048 0 950000048 0 92500007241 166666746 gt 0 80000007240 73 33338540 95833337341 04166674641 25000011941 45833337 gt 1 98333346841 091666698 0 95000004841 21666669841 200000048 2 36394453 gt 3 24475717542 93003988342 27639436741 74797308441 42541265541 09043073 Figure E 2 Example of a correctly formatted tab delineated file Most Common Mistakes in Pasting e forgetting the title e not using parentheses e not having parameters e using unnormalized data e having extraneous columns e forgetting to indicate parameters having non numeric parametric values with an asterisk Copyright 1998 2001 Silicon Genetics Appendix F 3 Copying and Pasting Experiments Copying an Experiment or a List Out of Gene Spring using more than one type of decimal marker or the wrong type for your computer s settings If your computer is set for a non English language that typically uses commas for decimal markers GeneSpring will recognize this If for example your computer is set for Fre
310. nch the comma will be recognized as a decimal marker You cannot use comma and periods inter changeably For details on changing the language settings in GeneSpring please refer to The Miscellaneous on page B 5 Pasting your Experiment into GeneSpring If you have not already give your experiment a unique name If it turns out it is not a unique name then GeneSpring will append a number on the end to distinguish it from other experiments of the same name You can copy Ctrl C all or part of a correctly set up Excel or tab delineated file In the main GeneSpring window go to Edit gt Paste gt Paste Experiment GeneSpring will automatically update the window regardless of which display options you cur rently have active Larger files may take longer to paste depending on your system WARNING Some computers will have a limit on the amount of data you can place on the clip board If you are consistently crashing at the point you may need a Java virtual machine update GeneSpring will bring up a new Choose Experiment Name box with the current name of the experiment already in the Name text box GeneSpring will take you back to main window with your new experiment already on display From here you can alter the normalizations with Experiment gt Change Normaliza tions command or alter the interpretation with the Experiment gt Change Interpre tation command Copying an Experiment or a List Out of GeneSpring
311. ndix N 2 Technical Details on the Statistical Group Comparison For Each Gene Then compute 2 i w nfi the group weights G W gt the sum of weights ial G a P gt x X ___the weighted mean W 2 BSS gt Fa F the between groups sum of squares d G 1 the numerator degrees of freedom BMS BSS 7 the between groups mean square 1 Z 1 G _ Wi 2 aA A fo 1 d the denominator degrees of freedom ifd is not greater than zero then exit p value 1 WMS 1 2 G 2 Z the within group mean square W BMS Sus the test statistic The approximate p value is calculated by looking up W in the upper tail probability of an F distribution with d and d degrees of freedom Note that d will not in general be an integer Nonparametric Analysis For the nonparametric analysis Replace each X by R their rank out of all of the X for the gene Perform the same anal ysis as for parametric test with variances equal P values are approximate but asymptotically accurate Copyright 1998 2001 Silicon Genetics Appendix N 3 Technical Details on the Statistical Group Comparison References References Brown M B and Forsythe A B 1974 The small sample behavior of some statistics which test the equality of several means Zechnometrics 16 169 132 Conover W J 1980 Practical Nonparametric Statistics 2nd Ed New York John Wiley amp Sons Inc Scheffe H 1959 The Analysis of Vari
312. ne is optional but if you are using a GenBank file an EMBL file or a seq file to define your organism s sequence then the sequence data will not be loaded into Gene Spring if this line is not in the genomedef file If your organism has not been sequenced or you do not have its sequence information available then you do not need to enter this line in the genomedef file KnowGenome set to true if the genome is sequenced and false if not KnowGenome true Copyright 1998 2001 Silicon Genetics Appendix l 3 Installing a Genome from a Text File The genomedef File 9 Ifthe genome you are entering is a circular genome such as bacteria plasmids and viruses then you should answer true to this question This line is optional if you do not enter it or answer it false then your genome will not be plotted as a circle in the physical position dis play CircularGenome set to true if the genome should be plotted as a circle and false otherwis CircularGenome true 10 Are there web based databases you would like to be able to link to automatically If not skip this question You can link to the URL of any web based database containing the name of your gene Each separate link should consist of one line in the genomedef file Each line should start with the phrase GeneHypertextLinks followed by a colon followed by the description of the link The description of the link is the name of the link the name y
313. ne of your genes If you would like to have any such links select the Yes circle Inthe Enter number of links box type the number of web databases you want to link the genes in this genome to When you enter a number in this box the number of Button lines in the table below changes In the first column of this lower table titled Button label1 enter the name of the web database as you wish it to appear on a button within Gene Spring In the right hand column titled URL enter the URL of the database with the system atic name of the gene replaced by a semicolon If the semicolon representing the place the systematic name of the gene should go is at the end of the URL it may be omitted You can also have links using names other than the systematic gene name To use one of these attach a special character before the link name in the Button label column Do not put a space or other character between the special character and the link name To use the common name use a dollar character To use the GenBank Accession Number use a percent sign To use the systematic name less anything after a dash use the dash a Select the Yes circle and the Next button if you have databases on the World Wide Web you would like to easily access from GeneSpring If you want to place more buttons you can change the number in the Enter number of links option Then use the tab key to move through the Button Label table Appendix C 4 Copyright 19
314. ne using any of the data in the corresponding columns A Systematic Name B Common Name and F Product You can also describe genes in your overlay or do a search for a gene named in column 2 Common Name and find the corresponding accession number The titles are included here only for clarity Remember when you are using the mapped format you must include any blank fields in their appropriate columns The gene s systematic name should always be in the first column its common name in the second and its mapping informa tion in the third column and so on even if the second column is completely blank because there are no common names for any of your genes GenBank or EMBL Files If you use a single GenBank file to describe the genome you do not have to use a Master Gene Table and therefore do not have to enter any of the information discussed in What Format do these Data Need to be in on page 1 Nor do you need a separate file to contain the sequence data the files for sequence data are described in Sequence Data on page 5 The GenBank file can be downloaded directly from GenBank if you open a web browser to the URL of the organ ism you are installing For example ecoli gbk is a 9 5 MB file from the URL ftp ncbi nilm nih gov genbank genomes bacteria Ecoli Generally this URL is the same for all of GenBank s bacterial genomes with the name of the organism you are installing in place of Ecoli Thi
315. neighbors and specify 1 in the P value cutoff field Copyright 1998 2001 Silicon Genetics 5 16 Exporting GeneSpring Data Chapter 6 Exporting GeneSpring Data You can save a GeneSpring image and import it into a graphics or other program where you can polish it and format it for publication GeneSpring saves images of pathways Venn diagrams the genome browser and the colorbar as pct files which can be imported into Microsoft Power Point Word Publisher Excel CorelDRAW and Adobe Illustrator among other pro grams To Save a Genome Browser Image 1 Display the image you wish to save in the genome browser This may be an image of a path way 2 SelectFile gt Save Image and choose Browser The Setup Graphic Size window will appear 3 Choose an image size from the Overall size pull down menu You will have the following options e Original Image Size lets you save the image exactly as it appears in the genome browser e Original Aspect Ratio allows you to change the image size but maintain the original width to height ratio displayed in the genome browser e US Letter 8 5 by 11 inches e US Legal 8 5 x 14 inches e A4 8 3 x 11 7 inches e 3 Foot by 5 Foot Poster 3 ft by 5 ft e Custom allows you to save to any size up to 450 inches by 450 inches 4 Choose a Margin Size If you choose Custom you will need to enter a percentage in the Enter percentage box 5 Choose a Mode either landsc
316. neously The cursor will change to a bull s eye 2 Release the keys and use the mouse to click on the window This will create a screenshot of your window you will hear the sound of a snapshot The screenshot will be saved on your hard drive with the name Picture 3 Open the picture and print Exporting Gene Lists out of GeneSpring You can make gene lists and annotated gene lists available to another application An annotated list includes functional descriptions as well as standard deviation standard error and other infor mation associated with the gene list To copy a gene list 1 Select the gene list you wish to copy from the Gene Lists folder in the navigator 2 SelectEdit gt Copy gt Copy Gene List 3 Paste the list into another application such as a spreadsheet program Or 1 Open the Gene List Inspector Double click a gene list or right click and select Inspect 2 Click the Copy to Clipboard button 3 Paste the list into a new application Both of these methods will export the default interpretation of your gene list To copy an annotated gene list 1 Select the gene list in the Gene List folder in the navigator 2 SelectEdit gt Copy gt Copy Annotated Gene List A menu will appear 3 Choose an experiment interpretation from the Copy based on interpretation pull down menu See Changing the Experiment Interpretation on page 2 17 for information on experiment interpretations Copyright
317. nes are not easily distinguished Finding Genes 1 Goto Edit gt Find Gene The Find Gene window will appear 2 Type a keyword systematic name or common name of a particular gene in the Find Gene win dow text box 3 Click OK or press the Enter key If GeneSpring does not recognize the word you typed in you may get an error message In some views the genome browser will zoom in on the found gene This gene will be automat ically selected If your search results in more than one matching gene GeneSpring will provide you with a list to choose from To reduce the number of matches type a whole word into the Find Gene box A par tial word like prot will result in a list with every instance of the string prot in it The more specific you can make your search string the fewer numbers of genes you will have to sort through in the Multiple Results window Copyright 1998 2001 Silicon Genetics 3 4 Viewing Data in GeneSpring Finding and Selecting Genes Selecting Genes Often you will need to select a gene or group of genes in order to identify gene names or quickly access genes you are working with To Select a Single Gene e Click once on any line or square representing a gene The name of this selected gene will appear in the upper right corner of the genome browser e Double click a gene to bring up the Gene Inspector window see Gene Inspector on page 3 37 or use Ctr1 TI for a selected gene This works o
318. netics 4 41 Analyzing Data in GeneSpring External Programs e DebugInput optional true if you want the data that is passed to the external program to be displayed in the Java console For example DebugInput true e DebugOutput optional true if you want the data that is passed from the external pro gram back to GeneSpring to be displayed in the Java Console For example DebugOutput true 3 Place the programdef file in the Programs folder in your GeneSpring Data directory Examples External Program Interface Example 1 SAS for Windows This example demonstrates how to use GeneSpring s external program interface The External Program Interface will export GeneSpring experimental data run a SAS program to analyze it and bring the results back into GeneSpring for display This example has been developed with Windows 2000 but should work with earlier versions of Windows It uses SAS Version 8 and you will need to change it somewhat to work with earlier versions of SAS This particular example sets up an interface to the SAS procedure FASTCLUS to do gene clus tering You will need to create three text files with a text editor such as Microsoft NotePad These files are FASTCLUS programdef Runsas bat and Fastclus sas These are each described below The first line of the description gives the name of the file including the proper file exten sion and the location where the file should be placed The file plac
319. neties Organization Demo User Identifier local 1 Created Tue Jun 19 10 22 27 PDT 2001 Notes Parameters pa A _ time minutes 0 10 20 30 40 50 60 70 80 100 110 120 130 Change Default Interpretation All Samples Normalizations There was no per sample positive control used Each gene was normalized to itself by making a synthetic positive control for that gene and dividing all measurements for that gene by this positive control assuming it was atleast 10 0 This synthetic control was the Change median ofthe gene s expression values over all the samples Copy l Data Range Attachments view File OK Cancel Figure 3 20 The Experiment Inspector window Copyright 1998 2001 Silicon Genetics 3 41 Viewing Data in GeneSpring The Inspectors The upper section of the Experiment Inspector contains the experiment information Most of the text in the white boxes are directly editable You can type copy and paste as you do with any nor mal text editor The Parameters box Within the parameters box you can view the various parameters for the experiment and their pos sible values Selecting the Change button in the parameters box will result in the Change Parameters window Please refer to Change Experiment Parameters on page 2 8 for details on this window Any changes made in the Change Parameters window will be saved and affec
320. ng Scatter Plot View Scatter Plot View The Scatter Plot view is useful for examining the expression levels of genes in two distinct condi tions samples or normalization schemes For instance you can use the scatter plot to identify genes that are differentially expressed in one sample versus another A scatter plot can also be used to compare two values associated with genes in two gene lists Such associated values might include the relative contribution of principal components as determined from principal compo nents analysis or two similarity scores from the Find Similar function in the Gene Inspector S GeneSpring 4 1 Yeast Genes all genomic elements File Edit Views Experiments Colorbar Tools Annotations Window Help i time 20 0 minutes EN raw Selected YMR1 ooh NW _ PIR keywords f all genes all genomic elem 2 ACGCGT in all OF like YMRigaw c 100 EH Experiments 1000 Random Data tirr VE east cell cycle ti 10 Gene Trees Experiment Trees Classifications Pathways Array Layouts Drawn Genes External Programs 0 1 Bookmarks Scripts so on nO o xm time 20 0 minutes control 1 10 100 1000 Trust time 20 0 minutes Animate Magnification 1 Zoom Out is f sf mmj Figure 3 7 The Scatter Plot view In the scatter plot in Figure 3 7 each symbol represents a gene The vertical position of each gene represents its expression leve
321. ng Self Organizing Maps 5 Choose the number of iterations This parameter controls how many times each gene is exam ined If there are 10 000 genes and 60 000 iterations are specified then each gene will be examined six times 6 Choose the starting neighborhood radius This parameter controls how many nodes move toward a data point at the beginning of the iteration and therefore how similar the profiles will be for each node As the iteration proceeds the neighborhood radius decreases smoothly so that points move more independently later in the process The neighborhood radius is expressed in terms of Euclidean distance in grid units relative to the abstract grid of the expression patterns This is different from the distance between nodes in gene expression space For instance point 1 2 is one unit away from 1 3 If you make the neighborhood radius very small less than 1 each point will always move independently and adjacent clusters will not be related If you specify a very large neighborhood radius initially all the nodes will move toward every data point and the grid will act as if it is very stiff with more similarity between node results but less flexibility to explore the variations in the data 7 Click Start When the analysis finishes the Choose Classification Name window will appear 8 Despite the name of the window you can save the result either as a classification or as gene lists by selecting one of the two Sa
322. ng can incorporate this data Select the Yes circle e In the first column enter the column name s or number s of the column s containing the pass fail information in the Flag column name box e Inthe second column Passed Designator enter the value given in the Flag column indicating the experiment worked for any particular gene Frequently the designator for good data is P for Present Passed or O for OK e Inthe third column Marginal Designator enter the value given in the Flag column indicating the experiment might have worked for any particular gene Uncertain or mar ginal data is normally indicated by an M e Inthe fourth column Absent Designator enter the value given in the Flag column indicating the experiment did not work for any particular gene Failed or absent data is normally indicated by an A When you are entering a column name be sure to use the spelling and capitalization used in your experimental data file If you have many rows and your designators are the same in every file click the Guess the rest button to fill down the table a Select the Next button to continue 18 The Sample Photos panel will appear This panel tells GeneSpring if you have any pictures you wish to associate with any or all of the samples Pictures are nice but they are not neces sary If you do not have any leave the No circle selected and proceed to the next panel If you have one or more pictures to associ
323. nging the Experimental Data Range on page 3 36 for more details on this topic Copyright 1998 2001 Silicon Genetics Appendix B 2 Preferences WindowColor e Structure color The Structure Color is used for the ConditionLine and for the lines between the genes in the Physical Position View the Tree lines the Ordered List lines etc e Background Color The Background Color defines the color behind the genes and other ele ments in the genome browser e Selected Color The Selected Color is used for selected genes gene names and axes For this you will probably want the greatest contrast with the background color For more information on the various color options on GeneSpring please refer to Changing the Coloring Scheme on page 3 31 Specific Color Definition A new feature in GeneSpring version 4 1 is the ability to define exactly what color you would like to use in the genome browser If your printer requires exact color definitions your life should be much easier after this To change or adjust a color in GeneSpring select the Change button next to its element in the Preferences Colors window Upregulated Color Ea Upregulated Color COLOR PREVIEW spena Color Figure 4 2 Color creation in the Preferences window Using your cursor click over any slider and move horizontally to adjust the color Keep an eye on the color preview box and stop moving the cursor when the desired color is reached Click OK to ac
324. nical service department at support sigenetics com or call 650 367 9600 Show Drawable Gene This command will bring up the straight line of a manipulatable pseudo drawn gene Please refer to Creating Drawn Genes on page 4 22 for more infor mation Find Interesting Genes This function finds genes with the greatest trust values who go through the largest expression changes during the experiment Please refer to Find Interest ing Genes on page 4 21 for more information Find Potential Regulatory Sequences This command initiates the Find Potential Regula tory Sequence window which allows you to specify certain parameters for an oligomer search in the nucleotide sequence preceding the genes in the list being displayed in the genome browser and to perform the search For more information about this window see Regulatory Sequences on page 4 26 If the nucleotide sequence has not been loaded a window will tem porarily appear saying Please wait while the nucleic acid sequence is being loaded Principal Components Analysis For information on Principal Component Analysis PAC please refer to Principal Components Analysis on page 5 5 or contact Silicon Genetics technical service department at support sigenetics com or call 650 367 9600 GeneSpider This command will activate the GeneSpider You can choose one of the avail able databases to update your information The GeneSpider will do an automatic web search
325. nt having expression profiles within a 95 correlation to YMR199W CLN1 s profile The genes fitting the restrictions of the search are listed in the top box The lower box titled Sim ilar lists contains the lists GeneSpring is aware of that are statistically similar to your new list Similar means the lists contain a statistically significant overlapping of genes How statistically significant the similarities are is given in the left hand column of the bottom box which lists the P value the probability of a false positive for each of the lists in the right hand column The p value of a statistically significant list is at least 0 05 By double clicking any item in the gene list or in the lists list you will bring up an Inspector for the selected item Copyright 1998 2001 Silicon Genetics 4 11 Analyzing Data in GeneSpring Filter Genes Analysis Tools Commands in the New Gene List window e Name The current default name is highlighted when the New Gene List window first appears ready to be changed e Save Cancel Clicking the Save button saves the list and the name in the name box in the example this name is likeYMR19W CLN1 0 95 and also displays this list in the genome browser display The Cancel button discards the list e Inspecting a Gene in the Gene List Box Double clicking a gene in the right hand box brings up a Gene Inspector window for that gene See Gene Inspector on page 3 37 for a complete descr
326. ntrol signals are located UseReferenceAsStrength enter true or false UseReferenceAsStrength false Normalizations Positive Controls 29 Do you have any genes designated as positive controls on your array You typically have pos itive controls when there is DNA from a different genome than the one you are investigating on your array and you added a known quantity of that DNA to your sample Entering true as the object value of the line given below means you have positive controls and you want GeneSpring to normalize your experiment using the positive control values This normaliza tion method takes the average signal intensities of all of the positive controls and divides each gene s signal intensity by that number for more information about this normalization option see Normalizing Options on page G 1 If you do not want to normalize your experiment using positive controls either do not enter the NormalizePosControl line or type false as the object value NormalizePosControl either true or false NormalizePosControl true The required layout file for positive controls 30 If you do not have positive controls or if you are not using them to normalize your data skip this question and the associated experiment file entry If you are using positive controls you must have a layout file and a file specifying what the positive controls are this second file must have the gene names of the positive co
327. ntrols written in a list one gene per line See sec tion The Layout file on page K 2 for more information about these files Specify the com plete file name of the layout file with the line below Layout complete name of the layout file the file name can be anything with or without spaces Layout AffyYeastLayout4 txt Appendix J 16 Copyright 1998 2001 Silicon Genetics Installing from a Text File Normalizations Each Sample to Itself There are two normalization options requiring you to have a layout file both use the same line to tell GeneSpring where to find the file You should only have one layout file and you should only enter the line Layout name of layout file once You may have already entered this file please refer to The required layout file for Region Specifications on page 9 31 If you do not have positive controls or are not using them to normalize your data skip this question and the associated experiment file entry Sometimes something will go wrong with the positive controls and you will get very low values for all of them which you will not want to use for normalization purposes Indicate the minimum average the positive controls must have such that dividing each genes control strength by the average of the positive controls will not artificially inflate the noise of the genes NormalizeMinRange indicate the minimum average allowable for the positive controls NormalizeMinRange 1
328. number of bases upstream the oligomer is from the ORF associ ated with it in the first column This number is the difference between the base pair num ber of the first base in the gene and the base pair number of the first nucleotide in the motif It includes the distance of the promoter This means the distance number is the dif ference between the promoter sequence and the ORF e Sequence This contains the sequence being examined written in bold On the left side of it are the ten bases proceeding this instance of the motif and on the right side are the 10 bases that follow it in the nucleotide sequence Copyright 1998 2001 Silicon Genetics 4 30 Analyzing Data in GeneSpring Making Lists of Homologs and Orthologs Making Lists of Homologs and Orthologs GeneSpring s Translate feature creates a gene list in a separate genome containing genes related to genes in the current gene list This allows you to compare genes with the same function homologous or orthologous genes in different organisms In practice however you may choose to define any two genes in different genomes as being related To make lists of homologs or orthologs 1 Open the GeneSpring data folder then open the folder of the organism you wish to translate from Create a new folder inside this folder and name it Homology Tables Create a text file and save it to the Homology Tables folder In the first column of the text file insert a unique identifier found
329. oarray technology report negative control strengths This is usually the result of subtracting estimated background levels that are larger than the raw signal This can happen in situations where the expression levels of the gene are low compared to the measurement error It can also happen when there is background subtraction or when a mismatched probe set has higher intensity levels than the perfect match probe sets If negative signal levels occur in a large fraction of the data used for normalization there can be problems with the normalization as the median across the normalization set can be very small or even negative This leads to unreasonable results of normalization In such cases which only occur in a few situations GeneSpring does an extra step in the normalization where it readjusts the background level for that data by adding a constant to all the raw control strengths in such a way that the 10th percentile of the signal is set equal to 0 before proceeding with the median nor malization This correction called the affine background correction is applied only when the 10th percentile of the data is more negative than the median of the data is positive You will get a warn ing message when you first load your data into GeneSpring if this background correction has been applied Also in the Gene Inspector raw control strengths adjusted by this correction are flagged with asterisks Whether or not the above correction is applied negat
330. of variance ANOVA You can specify whether to assume within group variances are equal across all groups Calculations without the assumption of equality of variances are done using Welch s approximate t test and ANOVA Non parametric comparisons are also available corresponding to the Wilcoxon two sample text also known as the Mann Whitney U test for two groups and the Kruskal Wallis test for multiple groups For Each Gene For each gene separately GeneSpring will do the following Let i index over the G groups formed by distinct levels of the comparison parameter Let Xj be the expression values with k running over the replicates for each situation interpreted according to the current interpretation ratio log of ratio fold change Let N the number of non missing data values for each group N Ay UN gt Xa be the group means and kel N SS S Er X be the within group sum of squares kal In all calculations here missing NaN values are left out of the sums not propagated If any of the N are zero drop that parameter level from the analysis and readjust G accordingly If G is not at least 2 exit p value 1 1 Filtering genes based on a one sample t test of the mean expression level across repeats or replicates ver sus a reference value can be done by selecting t test p value as the filter criteria in Expression Percent age Restriction Appendix N 1 Copyright 1998 2001 Silicon Genetics
331. ol channel value to indicate the trust you have in your experimental data you probably want to normalize the genes by dividing their signal strength by the control s signal strength The formula for this normalization option looks like this signal strength of gene A in sample X control channel value for gene A in sample X In two color experiments the control channel is often a green signal If you normalize to the con trol channel for each gene you may also want to normalize each sample to itself or to a positive control This will provide a control for sources of variability affecting the whole chip for exam ple variations in the amounts of dye added etc You probably do not however need to normalize each gene to itself or to a single control sample Copyright 1998 2001 Silicon Genetics Appendix G 3 Normalizing Options Gene Normalize to Control Channel Values for Each Mathematical Illustration of the Normalize to a Control Channel Value for Each Gene Method Given raw data with a Control Channel Raw Experimental Results Gene Sample 1 Reference Sample 2 Reference Sample 3 Reference Name l 2 3 CLN 1 1000 1000 2000 2000 1500 500 CLN2 1000 1000 2000 2000 500 500 CDC28 100 100 200 200 50 50 HSL1 1000 1000 2000 2000 500 500 YGP1 10 000 10 000 20 000 20 000 5000 5000 The results of normalizing to a control channel for each gene Af
332. ology _ PIR keywords all genes all genomic ele like Drawn Gen like YMR199W like YMR199W t 3 ONCO_PREDIC Experiments Gene Trees Experiment Trees Classifications Pathways Array Layouts Drawn Genes External Programs Bookmarks Magnific Tools Annotations Window Help time 0 minutes ation 1 Sso ono wn oOo nro x m Animate zoom Out a R Figure 3 9 Ordered List View To reach the following commands right click in the genome browser and select the Options drop down menu Color by Single Condition Color by All Conditions Allows you to visualize your data one condition at a time where the slider dictates the condition as in the Graph view or to visualize all conditions at once where conditions are layered one on top of the other and the slider has no relevance Copyright 1998 2001 Silicon Genetics Show Hide Associated Values Shows or hides your associated values 3 21 Viewing Data in GeneSpring Array Layout View Array Layout View The Array Layout view produces a synthetic picture of the arrays used in the current experiment This view is useful in identifying arrays that display local shifts in intensity due to problems in probe deposition hybridization washing or blocking To use this view you must first create an array layout file see Creating an Array in GeneSpring on page M 1 File Edit View E
333. olorbar on the right You can change the colors to any of the standard coloration options Color by all Conditions Color by a Single Condition In the Color by a Single Condition option the genes in the gene tree are colored according to their expression at the condition indicated by the scroll bar at the bottom With the Color by all Conditions option the genes in the gene tree are colored corresponding to each condition in the experiment as shown by the name of the continuous parameter displayed at the right of the screen The beginning of the experiment is colored at the top of the gene next to the green line and proceeds chronologically downward To Color by all Conditions 1 Right click while the cursor is in the genome browser 2 A menu will appear go to the Options submenu 3 Select Color by all Conditions To Color by a Single Condition 1 Right click while the cursor is in the genome browser 2 A menu will appear go to the Options submenu 3 Select Color By Single Condition Once your experiment is colored by single conditions you can use the animate feature 1 Select the Animate checkbox a check mark will appear in the box when selected Or 1 Move the slider along the bottom of the main GeneSpring screen It may take a second or so for the tree to redraw when the time changes because of the complexity of the picture Viewing Parameters in Trees For most experiments each measurement was taken under certain
334. omes Selecting will bring up a new main GeneSpring window with your chosen genome displayed New Pathway This command will bring up the New Pathway Wizard Please see Path ways on page 4 23 for more details Save Bookmark A Bookmark will save your analysis at its current point so you can come back to it later Save your bookmark by selecting File gt Save Bookmark You will need to input a name for the bookmark To open your saved Bookmark go to the Bookmark folder and select a bookmark to view The File drop down menu also gives you several options for loading genomes and experiments into GeneSpring please refer to the GeneSpring Loading Data Manual The Edit Menu Copy The copy menu allows you to copy gene lists experiments or fully annotated gene lists to the clipboard if the experiments are properly set up Please refer to Copying and Pasting Experiments on page F 1 for more details Paste The paste menu allows you to insert an entire experiment from the clipboard if the experiment is properly set up Please refer to Copying and Pasting Experiments on page F 1 for more details Find Gene A particular gene can be found directly by using Edit gt Find Gene type either the systematic or the common name in the Find Gene box then click OK or depress the Enter key The genome browser will be zoomed around your selected gene You can also type in a keyword such as immun and GeneSpring will present you with
335. ompany or institution e The Desired Memory field sets the amount of RAM GeneSpring will attempt to use If this field is set too high with respect to the total available memory unnecessary disk caching will occur and performance will be slowed e The Disk Cache Size field specifies the amount of hard disk space GeneSpring uses to store HTML pages accessed by the GeneSpider or by other internet based search func tions The Miscellaneous The Miscellaneous panel contains a grab bag of defaults to customize your GeneSpring installa tion e The Default Correlation field specifies the default minimum correlation coefficient that appears near the Find Similar button in the Gene Inspector window e The Restrict Gene List Searches drop down menu allows you to limit the lists GeneSpring examines when searching for similar lists in the Gene Inspector window and during Tree building e The Default Font field allows you to specify the name style and point size in this order separated by hyphens for most of the text within the GeneSpring window When you first install GeneSpring the name and style fields are left blank and only the point size is spec ified e g 9 An example of an alternative font specification might be Serif Bold 12 The available font styles are plain italic bold and bolditalic The available font names differ depending on what JVM you are using Start with the generic font classes Serif
336. on to the Correlations box select the experiment or condition in the Experiments folder in the navigator and select the Add button under Correlations Add ing a new experiment or condition will bring up the New Correlation window On the right side of the window is a cumulative distribution graph of the genes correlations The horizontal axis shows the correlation from zero to 1 the vertical axis depicts the number of genes The green lines are your specified maximum and minimum values If you change these values the green lines will move accordingly a The Phase Offset series variable function in the upper left corner of this window specifies how far the expression profiles should be offset in time or other continuous parame ter from the expression profile of the gene to be correlated against This function is optional You can change the selected parameter to be offset by selecting a variable from the drop down box b You can also select a weight for your experiment or condition which is a measure of the influence the experiment or condition has on the correlation distance For example an experi ment with a weight of 2 0 will be twice as influential as one with a weight of 1 0 For this equation please see The Correlations box on page 4 16 c You can also weight each gene by signal strength with the result that each gene will have a different weight To do this click in the box marked Weight by Control Strength d Click
337. on window will appear You can also right click the genome browser in graph view and select Options gt Change Experiment Interpretation e From the top pull down menu choose a data display mode for the vertical axis Ratio signal control Log of ratioorFold Change The mode you choose will be used in such statistical procedures as Statistical Group Comparison k means Cluster ing Self organizing Maps and Principal Components Analysis See below for details on these modes Choose the lower and upper bounds of the vertical axis in the fields pro vided e Ifyou do not wish to use the Global Error Model deselect the Use Global Error Model checkbox Using the Global Error Model allows you to produce a better estimate of precision You can use these estimates in a number of analyses including filtering and clustering For information on the Global Error Model see Global Error Models Techni cal Details on page N 1 For details on Color by Significance see Color by Signifi cance on page 3 33 e Depending on your instrumentation you may have flags indicating the degree to which your data is reliable If you have flags choose from the Use Measurements Flagged pull down menu to limit data based on these flags e Choose a mode for each parameter Continuous Element Non continuous Replicate orColor Code Note that if you choose Color Code you must also select Colorbar gt Color by Parameter See below for details on these modes
338. ons Window Help E Gene Lists i Chromosome G Gene Ontology GHL PIR keywords all genes all genomic elem Y ACGCGT in all OF 8 like YMR199W C EH Experiments HHE Random Data tirr E Yeast cell cycle ti EH Gene Trees EH Experiment Trees EH Classifications H_ Pathways Array Layouts _ Drawn Genes zH External Programs EH Bookmarks Sso on no GBxmM mito EH Scripts r 0 Trust time 20 0 minutes Animate Magnification 1 Zoom Out ol el 4 sf gt Figure 3 4 The Physical Position view The Physical Position view for yeast is also discussed in GeneSpring Basics Instructional Manual 1 3 Physical Position Display on page 1 5 At greater magnification you can see the base pairs Copyright 1998 2001 Silicon Genetics 3 10 Viewing Data in GeneSpring Physical Position View gt GeneSpring 4 1 Human Oncogenes Genes all genes Fie Edit Views Experiments Colorbar Tools Annotations Window Help H Gene Lists L m EH Experiments E Gene Trees HJ Experiment Tre EH Classifications EH Pathways Array Layouts EH Drawn Genes IHL External Progr E Bookmarks HJ Scripts Sso on no BoB x mMm Trust 0 Animate Magnification 1 Figure 3 5 Physical position view for human oncogenes Copyright 1998 2001 Silicon Genetics 3 11 Viewing Data in GeneSpring Physical Position View Fie Edit Yiew Experiments Colorbar Tools Annotations Window Help EH Gene
339. opy by going to the following url http www sigenetics com cgi SiG cgi Products GeneSpring download smf Follow the on screen directions and Silicon Genetics will send you a username password and download link Starting GeneSpring Once you have installed GeneSpring you will find two new items on your desktop the GeneSpring Data folder and the GeneSpring icon Copyright 1998 2001 Silicon Genetics 1 1 Introduction Bo 19 Z GeneSpring shortcut to GeneSpring Data Figure 1 1 The GeneSpring Data and Start icons To start GeneSpring double click the GeneSpring icon Alternatively Windows users can reach the GeneSpring icon by selecting Start Programs GeneSpring or Program files Silicon Genetics GeneSpring Mac users can also start GeneSpring from the Applications folder Silicon Genetics GeneSpring A splash screen will appear containing your GeneSpring version number the expected expiration date and the JVM you are using You will then see the GeneSpring main window For further details see GeneSpring Basics on page 1 7 Obtaining a License Key If you have already installed a demo copy of GeneSpring your license key will expire within two months Once you have purchased a full GeneSpring license Silicon Genetics will send you a license key Save this license key file in the Silicon Genetics GeneSpring Data folder See The GeneSpring Hierarchy of Objects or Where Is My Data Stored on page 1 15 for
340. or example a compact range of data This range refers to the variability in a single condition not in the mean expression level over an entire experiment NOTE If your original data did not include measurement flags you can use the Range of normalized data feature to filter out Absent genes by specifying a value 0 or above because Absent genes are not assigned any value e Standard Error of Normalized Data the precision in an experimental condition as expressed in terms of standard error e Standard Deviation of Normalized Data the precision in an experimental condition as expressed in terms of standard deviation Silicon Genetics recommends three methods for fil tering for reliable genes using the Standard Deviation of Normalized Data option e Ifyou want genes where the standard deviation of the individual normalized measure values is less than or equal to a maximum value L specify L as the maximum value e Ifyou want genes where the mean of the normalized values in each group has a stan dard error of L or less specify L sqrt N as the maximum value where N is the num ber of replicates in each group e Ifyou want genes where the mean of the normalized values in each group is accurate to within L with 95 confidence then specify L sqrt N 1 96 as the maximum value again where N is the number of replicates in each group e T test probability the likelihood that the difference between the normalized expression level an
341. orresponding to an eigenvector represents the amount of variability explained by that eigenvector The eigenvector of Copyright 1998 2001 Silicon Genetics 5 5 Clustering and Characterizing Data in GeneSpring Principal Components Analysis the largest eigenvalue is the first principal component The eigenvector of the second largest eigenvalue is the second principal component and so on Principal components which explain sig nificant variability are displayed by GeneSpring in the Principal Components Analysis window There will never be more principal components than there are conditions in the data Viewing Principal Components in a Scatter Plot After performing principal components analysis the genome browser displays a scatter plot in which the first and second principal components representing the largest fraction of the overall variability are plotted on the vertical and horizontal axis respectively This type of view is useful for selecting and making lists of genes that exhibit high levels one or two principle components Genes that exhibit high levels of the first principal component and low levels of the second princi pal component are displayed in the lower right comer of the plot and genes exhibiting equal lev els of the two components lie along the diagonal gt GeneSpring 4 1 Rat Genes all genes File Edit View Experiments Colorbar Tools Annotations Window Help Gene Lists jagg MA componant Experiments Gene Trees 7
342. ory If you have a separate sequence file put that in this new directory also If you have a file containing extra genes put that file in this new directory 4 Inthe same directory create a file describing the genome The file name should end with genomedef such as Ecoli genomedef See The genomedef File on page I 1 for what this file should contain 5 All files within the GeneSpring data directory except those in the cache directory if there is one ending in genomedef are found automatically Start GeneSpring to make sure your genome is properly loaded You should be able to find its name by selecting File gt New Genome In this example E Coli appears there Creating Folders for New Genomes To manually create a new folder in the genome browser you must go through a file management system such as Windows Explorer Before your new folder will appear in the navigator you will need to create a correct genomedef file for that organisms A genomedef file will contain all the information GeneSpring needs to create a folder and other data objects Make sure you save the genomedef file in your new direc tory after you create it The genomedef File The genomedef file contains a brief description of the genome This file contains several lines each of the form object name space colon space object value For example Object name object value An example of how this actually appears in the genomedef
343. osControls false Copyright 1998 2001 Silicon Genetics Appendix K 3 Experiment File Formats What format does this data need to be in 3 Include this line if your experiment has negative controls This line refers to a file listing the negative control If you have negative controls you must have a file designating them See The Positive and Negative Control Files on page 7 for information about this file NegControlFilename the complete file name of the file listing the gene names of the negative con trols one per line NegControlFilename NegControls txt 4 Include this line if a sample in your experiment involved more than one array or if there is some reason to normalize the sections of the array separately If the genes from a sample could belong to more than one region then the region must be noted somehow in the experimental data file see The Region Designation File s on page 4 Use this line if the region is noted as either a unique entry in its own column or if it is a suffix appended to another column s entry The object value s in this line refer to separate files each listing one possible region designator See The Region Designation File s on page 4 for more information Multiple region designation files should be separated with semicolons but not spaces Regions the complete file names of the files listing the region designations separated by semicolons Regions YeastRA txt Y
344. ou want to appear on a button in GeneSpring which must be followed by a colon not a semicolon Any field in angle brackets for example lt field gt will be replaced by the value of that parameter The allowed parameters are e systematic common e genbank e ec e pubmed e map e chromosome e synonyms e description e phenotype e function e product e keywords e dbid e custom e custom2 e custom3 A link will only be enabled for a particular gene if all parameters mentioned in that URL are defined for that gene GeneHypertextLinks Links to external web based databases You can have more than one of these lines you should have one line for each link GeneHypertextLinks linkname http www some where org gene lt systematic gt amp id lt genbank gt Appendix l 4 Copyright 1998 2001 Silicon Genetics Installing a Genome from a Text File The genomedef File This example should be one consecutive line beginning GeneHypertextLinks but is has been broken into separate lines to allow it to fit on this page It should be entered into the file as single line without carriage returns There is no space between the semicolon following the link s name and the associated URL Experiment URLs work exactly the same way except that they begin with ExperimentHyper textLinks instead of GeneHypertextLinks and the things in lt gt signs are the names of param eters A link will only be shown in the Exp
345. ou will find your new gene list in the Gene Lists folder Scripts Using Scripts New in GeneSpring 4 1 is the ability to automate complicated analyses with scripts GeneSpring 4 1 includes several example scripts to demonstrate the power and flexibility of scripting If you wish to design your own scripts you will need to install the Script Editor For information on pur chasing the Script Editor please visit the Silicon Genetics Web site at http www sigenetics com Products ScriptEditor To Execute a Sample Script 1 Inthe Navigator open the Scripts gt examples gt high correlations folder 2 Click one of the example scripts The Run Script window will appear 3 Choose the inputs that are required for the script by selecting a data object from the navigator panel and clicking the appropriate button in the Inputs box 4 Ifthe script contains knobs you will need to enter parameters to direct the execution of the script 5 Once all the inputs and knobs have been selected or entered click the Execute locally button at the bottom of the window You can access the Script Inspector by right clicking over any script and selecting Inspect Note If you have a connection to GeNet and are using Remote Execution Servers you have the option of having the script executed on a remote computer To run a script remotely do steps 1 4 as described above and click the Execute Remotely button What is a Script Scripts are tools that
346. particular character or characters b In the box that appears labeled Enter suffix marker character s enter the character mark ing the beginning of the suffix There may be multiple different markers indicating the begin ning of a prefix If this is the case enter them all in the Enter suffix marker character s box Do not separate multiple marker characters in any way Anything you use to separate the characters including empty spaces will be considered a suffix marking character and will be removed from the gene name along with any characters following it Make sure when you are entering a set suffix or a suffix marker character you get the spelling and capitalization exact c Select the Next button to continue The Data Column Location panel will appear This panel tells GeneSpring which column s of your experimental data files contains the genes raw data Enter the name or number of the column containing raw data in the Enter data column name box Make sure to use the correct spelling and capitalization for this entry If your data file includes a column containing the background signal to be subtracted from the gene s raw data in the second question Do your data files contain a column representing background control strength select the Yes circle Enter the name of this column or its number in the white Data Background Column on the right Again beware of spelling and capitalization errors This panel will not let you pro
347. pear directing you to choose a gene ID from a list Double click on the appropriate ID If you make a mistake you can right click on the gene you would like to remove and select Delete Pathway Element Copyright 1998 2001 Silicon Genetics 4 24 Analyzing Data in GeneSpring Pathways Adding KEGG Pathways When you import a pathway from KEGG Kyoto Encyclopedia of Genes and Genomes Gene Spring can use the associated html file to add relevant genes to the pathway Because GeneSpring locates these genes by EC number you need to have the EC numbers for your genes in your genome You can automatically retrieve these numbers from GenBank and LocusLink using GeneSpider To obtain the necessary KEGG files 1 Point your Internet Explorer or an FTP client to ftp kegg genome ad_ jp pathways 2 Copy and paste the map folder which contains organism independent pathways into the Pathways folder in the selected genome e g Program Files SiliconGenetics GeneSpring Data YourGenome Pathways The folders that correspond to organism specific pathways are not always recognized by Gene Spring because the annotation for some genes is in a modified format Finding New Genes on a Pathway GeneSpring uses proprietary algorithms to predict the genes that fit near a selected point on a pathway After you select a point GeneSpring makes two lists of genes from those currently dis played on your diagram List A contains the two genes that appear clo
348. pecified sequence in this example ACGCG in the yeast data there is a number associated with each gene cor responding to distance of the first such sequence upstream of the ORF The numbering begins from first nucleotide These numbers can be easily viewed by zooming in on the Ordered list view or opening the Gene List Inspector e Extend Promoter Adds a new longer and hopefully better promoter in the Find Poten tial Regulatory Sequences window Details box This box gives a general description of the common sequence motif being inspected The details found in this box are the same numbers listed in the right hand columns of the Results box in the Find Potential Regulatory Sequences window The Offset Bases box The middle third of the Conjectured Regulatory Sequence window contains statistics on the bases to either side of the motif The first column gives the offset from the observed sequence The next four columns give the percentage of genes with that base in that position The last column contains a suggested extension to the motif ORF Box The bottom third of the Conjectured Regulatory Sequence window contains the sequence information for the motif being inspected as it occurs in the nucleotide sequence in the area near or in each gene where it is found There are three columns of data e ORF This indicates the gene that the common sequence motif given in bold centered in the column is upstream of e Distance This gives the
349. pen window in the current run All of these prefer ences will take effect when GeneSpring is restarted Select Edit gt Preferences To change any options in the Preferences window select the drop down menu and choose the appropriate item Data Files Here you can set the defaults of what you would like to see when GeneSpring opens By setting the defaults in this box you can have GeneSpring open directly to your chosen experiment e Data Directory The default directory genome that opens at startup Use the browse but ton to select the settings e Default Genome To change the default genome that is loaded when GeneSpring first starts enter the name of a genome in this field Database If you plan to store your experiment s expression data in a database the Database panel allows you to specify the method GeneSpring will use to extract data from an ODBC compliant database The drop down menu selecting the black arrow will produce another option Parameters appearing to be numeric list individually allows you to specify how Gene Spring will assign the parameters for a series of numeric values in your database In addition you will need to specify the fully qualified classname of the driver in the JDBC driver field Appendix B 1 Copyright 1998 2001 Preferences WindowColor Color The Color panel allows you to change the colors GeneSpring uses to represent different types of data and other screen elements In this box you may
350. periment parameter 2 11 condition 2 13 multiple 2 12 parameter value 2 11 Experiment Wizard D 3 experimental data file K 1 explained variability 3 47 Export data by copying F 4 to External Program interface 4 40 to GeNet 6 6 expression values determining G 1 External Program interface 4 40 Copyright 1998 2001 Silicon Genetics F FAQ A 1 File Menu P 2 files database E 4 experiment J 1 gbk C 2 homology 4 31 layout M 2 seq C 3 FileAccess jar 4 44 Filter Genes Condition to Condition Comparison Re striction 4 7 Data File Restriction 4 7 4 8 Expression Percentage Restriction 4 3 Expression Restriction 4 7 removing restrictions 4 9 restricting data types 4 8 Find Gene 3 4 P 2 Find Potential Regulatory Sequence 4 26 Find Similar Genes 3 40 Finish D 16 Flags D 11 G 17 J 12 formula notation L 1 Functional Classification 3 27 clear or remove 3 28 G GATC E 2 GenBank Accession Number H 3 Gene Inspector 3 37 Control 3 39 Correlation Commands 4 14 Description 3 39 Normalized 3 39 notes 3 40 Raw 3 39 Save Profile 3 40 Student s t test 3 39 t test p value 3 39 Web Connections 3 40 Gene Name D 9 J 8 Gene Name Prefix Removal D 9 J 8 Gene Name Suffix Removal D 10 J 8 gene similarity L 1 Index 2 GeneSpider 2 15 P 4 lists from annotations 4 19 GeneSpring Basics Instructional Manual A 1 GeneSpring User Manual A 1 GeNet 6 6 GeNet Database A 2 Genome Browser printing 6 2 Genome Browser see also Browser display
351. periment refers to Clicking the Yes circle will cause another box to appear Type in the name of any subdirectory you would like to use for this experiment You may have more than one experiment within a folder c In the bottom box enter any comments or general notes you have about this experiment These notes will be visible and editable in the Experiment Inspector Please refer to Exper iment and Condition Inspectors on page 3 41 for more information about that window d Click the Next button to proceed to the next panel 4 The Number of Arrays panel will appear This panel tells GeneSpring how many single arrays or samples combine to make this experiment A single array is defined as each time a mea surement is taken of your entire set of genes a Select the No circle if there was only a single set of measurements taken OR a Select the Yes circle if more than one set of measurements for your genes were taken Selecting Yes in this panel will reveal a box to type in the number of arrays b Enter the number of measurements that were taken of your gene set by typing the number in the Number of Arrays box GeneSpring will not let you proceed if you click Yes but do not indicate how many Arrays Samples there are c Click Next to proceed to the next panel Appendix D 4 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard 5 The Number of Parameters panel will appear This panel tel
352. ple s each gene is divided by the intensity of that gene in a specific control sample or by the average intensity in several control samples The formula for this is signal strength of gene A in sample X signal strength of gene A in the control sample s signal strength of gene A in sample X average signal strength of gene A in several control samples To Normalize To Sample s 1 Under Per gene normalizations click Use sample s 2 Indicate the numbers of the control samples sample numbers are listed under Experi ments gt Change Experiment Parameters Multiple experiment numbers must be separated by commas e g 1 2 Ranges of experiment numbers can be indicated by a dash e g 1 3 5 You also have the option of normalizing subsets of your samples to the mean of specific subsets of control samples For more information click the Use sample s Help but ton 3 Specify a cutoff for the denominator in the above formula The cutoff is used on measurement values that have been partially normalized in previous normalization steps so this should be a small number like 0 01 If the denominator falls below the cutoff and the numerator is above the cutoff the denominator used for the above formula will be 01 If both the numerator and Copyright 1998 2001 Silicon Genetics 2 25 Creating DataObjects in GeneSpring Miscellaneous the denominator fall below the cutoff this measurement will not be included in the normaliza tion
353. r the Predictor Cover T M and Hart P E 1967 Nearest Neighbor Pattern Classification IEEE Transactions on Information Theory IT 13 21 27 Duda R O and Hart P E 1973 Pattern Classification and Scene Analysis Wiley New York Golub T R et al Molecular Classification of Cancer Class Discovery and Class Prediction by Gene Expression Monitoring Science v286 pp 531 537 1999 Copyright 1998 2001 Silicon Genetics Appendix O 2 Common Commands Commands Accessible by Cursor or Keyboard Appendix P Common Commands There are a number of common commands available in nearly all of the GeneSpring screens Not every command listed here will be available in every screen nor is every command available listed Commands specific to particular displays will be described in greater detail in those chap ters Commands Accessible by Cursor or Keyboard e Select You can select a gene by clicking it You can select more than one gene by clicking subsequent genes while holding the shift button down You can select all the genes in an area by left clicking in one corner of a rectangle and dragging to the opposite corner while holding down the Shift key If you know the systematic or common name of your gene you can select it by selecting Edit gt Find Gene e Gene Inspector Double click any gene in the browser to bring up the Gene Inspector Or if a gene is already selected you can use Edit gt View details on selected g
354. r3Experimentl9 Test eter3Experiment20 Test eter3Experiment21 Test eter3Experiment22 Test eter3Experiment23 Test eter3Experiment24 Test eter4Experimentl health eter4Experiment2 health eter4Experiment3 health eter4Experiment4 health eter4Experiment5 health eter4Experiment6 health eter4Experiment7 health eter4Experiments8 health eter4Experiment9 health eter4Experimentl0O healt eS Si Eat ees fea EE pe a pp Poe Ss RS PSS eS Se DO DO NO NHOPRHPRPRPRP RPP RP BP BE Pe ee Se RS eS ee y y y y y y y y y h y Define Your Parameters Copyright 1998 2001 Silicon Genetics Installing from a Text File Describe your Data Files Parameter4Experimentll Andromeda strain Parameter4Experiment12 Andromeda strain Parameter4Experiment1l3 Andromeda strain In order to illustrate how to write all four of the possible parameter displays the Yeast extraterres trial study is a fairly large experiment with many samples as well as many parameters This makes the entry for question 9 extremely long You may well have a much smaller and less com plex set of notations to write down Describe your Data Files 10 Are all of your samples in the same data file If so enter this DataFileName complete name of the file contain ing your experimental data DataFileName array txt If even one of your experiment s samples are in a separate file from the rest you must specify a
355. rce negative values to zero Forcing all of the negative numbers to zero converts all the negative values to zero after all the normalizations have been implemented and after the genes that do not pass the Pass Fail vote have been thrown out this happens before any nor malization is applied by GeneSpring The Finish panel will appear When you click the Load Now button all of the answers you gave in the previous Experiment Wizard panels are saved in an htmi file If GeneSpring is unable to load the data you will get an error message with a list of the unrec ognized genes that caused it not to load Appendix D 16 Copyright 1998 2001 Silicon Genetics Installing from a Database Custom Databases and GeneSpring Appendix E _ Installing from a Database Custom Databases and GeneSpring You can load experiments into GeneSpring from your company s database To do this you will need to set up a database file prior to starting the New Experiment Wizard Databases A database is an organized collection of information Essentially it is a collection of records In database terms a record consists of all the useful information you can gather about a particular item Each little bit of information making up a record is called a field An example of a non com puterized database would be your address book Each record represents one of your contacts and each record consists of many fields such as name address number and so on Computer data
356. rch calls for a type of analysis that GeneSpring does not perform The external program interface is also useful for parsing and pre formatting data for use in another application When you launch an external program from within GeneSpring the data that is displayed in the genome browser will be sent to the external program as standard input When the external pro gram runs GeneSpring recognizes the standard output generated by the external program and dis plays it in the genome browser To run an External Program 1 Select the gene list that you want to send to the program 2 If your program takes the data from a tree or a classification as input be sure these are selected and visible as well 3 Open the external program folder in the navigator panel and click the program you wish to run Copyright 1998 2001 Silicon Genetics 4 40 Analyzing Data in GeneSpring External Programs To install a new external program 1 Create or obtain an external program Any program capable of receiving standard input is acceptable 2 Create a file named XXXXXX programdef Each line of a programdef file should contain a parameter followed by a colon followed by the parameter value Blank lines and lines begin ning with the sign will be ignored GeneSpring recognizes the following parameters Name required the name of the external program as it will appear in the navigator For example Name Sort Gene List Icon optional the file
357. rd on page D 1 for details Choose an existing genome or create a new one 4 Choose an experiment name and click Save Your experiment will appear in the genome browser To set up Column Formats If GeneSpring does not recognize your file format you can use the Column Editor to assign head ings and functions to each column in your data file The Column Editor is programmed to remember the format of your file for the next time you load data with that format Note however that the Column Editor will not remember a format if you have more than one sample in a file or if you have more than one signal column 5 x ine 5 Hepatitis 0 ine 6 Type Hepatitis O r Line 7 unction gt Column Titles gt ine 9 n y y Move Headline Up a p Saey n N Move Headline Down Unassigned Unassigned Unassigned Unass Type Cancer n ajn Lines To Skip YALOO1C 0 941 666722 0 950000 Line 10 YALOO2VY 1 738317728 0 670093 7 Has Column Titles Line 11 YALOO3VY 0 710966289 0 964862 Line 12 YALOO4VY 3 33333349 1 960784 Sieso The RDG Line 13 YALOO5C 3 694501 762 3 197556 Tinei sf YALOD7C 0 684667 289 ae 1 637415 ana ee Lines vaLoosw 2 355 29583 1 692307711 2 047337 aaa Flag Values PassFlag P AbsentFlag e Marginal Flag M gt Please click on cells in the function row to assign functions to columns Click Load now when done FUNCTION PULL DOWN MEN
358. re details about each of these display options e Parameter 1 is continuous This means when you are graphing the data by this parameter the data points will be connected together by lines instead of being graphed as discrete points Follow Parameter1IsContinuious with true if this is how you wish the parameter to be graphed If one of the other possibilities seems more correct for parameter 1 either enter false as the object value or do not include the line beginning with Parameter 1IsContinuious Parameter IsContinuous ParameterlIsContinuous Parameter2IsContinuous Parameter3IsContinuous Parameter4IsContinuous either true or false true false false false e Parameter 1 is a category or set of categories and you wish to color code the display by their membership If this is the display you wish for parameter 1 answer the object name lines Parameter 1IsContinuous Parameter IsSet and Parameter IsRepeat all with the object value false This is the case for parameter 2 in the Yeast can cer time series experiment Copyright 1998 2001 Silicon Genetics Appendix J 3 Installing from a Text File Define Your Parameters e Parameter 1 is a replicate parameter by which you do not wish to distinguish information graphically Follow Parameter1IsRepeat with the object value true if this is how you wish this parameter to be graphed If one of the other possible
359. red halfway between the two color extremes Condition a grouping of one or more samples Control an experiment data set that provides a comparison or contrast to experimental results Control Strength see also expression strength the quantity divided by the raw value to get the normalized value Cluster a collection of genes that have been grouped according to a certain criteria such as simi lar mean expression values D Data Objects any downloadable or uploadable items in GeneSpring such as genomes gene lists classifications etc Dendrogram a diagram showing hierarchical relationships based on similarity between ele ments for example similarity of gene expression levels Appendix Q 1 Copyright 1998 2001 Silicon Genetics Glossary Drawn Gene lines representing gene profiles that you draw in the genome browser You can then search for genes matching that profile E Experiment a group of conditions associated together under one name This generally means they were all performed using a particular set of parameters Experimental Parameter a variable used to describe the condition or conditions during an experiment A set of parameter values defines a single experimental parameter When the word parameter is used alone it usually refers to an experimental parameter Experiment Tree a dendrogram used to show the relationships between the expression levels of conditions Experiment Specification Area
360. relation coefficient and the experimental points will be graphically similar to one another They are all on the same vertical scale rather than the same pattern of changes on widely differing vertical levels The formula used is the signal strength of gene A in sample X the median of every measurment taken for gene A throughout all of the samples Do not use this normalization method in concert with normalizing all samples to a specific sam ple as they are both intended to address the same issue If you are using GeneSpring to do all of your normalizations and you are not doing a two color experiment using this normalization method is highly recommended This normalization option is commonly combined with either normalizing each sample to itself or normalizing to positive controls As it is more striking math ematically to illustrate it as the second step of normalization there are two mathematical illustra tions one following the normalization of each sample to itself and the second following normalization to positive controls For explanations of either of these first normalizations see Normalize Each Sample to Itself on page 6 or Normalize to Positive Controls on page 5 You can specify a cutoff to prevent small and negative measurements from participating in the normalization The cutoff is specified in terms of measurement values that have been partially normalized in previous normalization steps so if your data has o
361. require some preparation such as creating a tree or adding a pathway or Array Layout image For details on views see Viewing Data in GeneSpring on page 3 1 e Zooming in To zoom in on a region or gene click on an area and drag your cursor diago nally You will see an expanding rectangle Release the mouse and GeneSpring will zoom in on the region enclosed by this rectangle e Zooming out To zoom out right click Control click for Mac and choose Zoom Out to go back one level or Zoom Fully Out to zoom out as far as possible e Moving around the screen You can move around a zoomed in screen by using Page Up Page Down and the arrows keys e Selecting a gene Click once on a single gene to select it e Selecting multiple genes Hold down the Shift key and drag to select multiple genes Or hold down the Shift key and click on individual genes to select them one by one e Finding a specific gene Select Edit gt Find Gene Type in the gene name or keyword and click OK GeneSpring will select and zoom in on the gene e Inspecting genes You can view detailed information about a gene by double clicking on it and bringing up the Gene Inspector window This is easier after zooming in on the gene A shortcut to the Gene Inspector is Ctrl I or I for Mac users e Undo You can undo your last action by selecting Edit gt UndoorCtrl Z2 Z for Mac users Your First Gene Lists To make lists from appropriate keywords 1 Sele
362. ress of the clustering Clicking the Start button will not close the Clustering window so you can begin planning another tree immediately For details on all the options you could change please refer to Cre ating Complex Experiment Trees on page 5 2 Changing the information given in the Clus tering window after you have started clustering a tree does not change the parameters of the tree in the process of being made Changing the parameters displayed changes the parameters required for the next tree you make from this window The Close button at the bottom of the window closes the Clustering window This will not halt the making of a tree currently in the process of clustering You cannot start clustering a new tree while there is already one in the process of being computed Copyright 1998 2001 Silicon Genetics 5 1 Clustering and Characterizing Data in GeneSpring Trees 4 The Name New Tree window will appear Name your tree and select Save 5 GeneSpring will automatically take you back to the main window where you can examine your new tree You may need to resize the window by clicking and dragging the edges in order to view the parameters You can also view another list in this same tree structure by selecting a new list from the Gene Lists folder Creating Complex Experiment Trees Complex trees can be made from multiple experiments or by tightly defining the types of data to use You can select a gene list the navigator to re
363. ric test global error model variances C Non parametric test Vilcoxon Mann vvhitney test Kruskal Wallis test P value cutoff 0 05 Multiple Testing Correction None x With 6 308 genes going into this comparison a p value cutoff of 0 05 and no multiple testing correction you can expect about 315 40 genes to be selected by chance OK Cancel Figure 4 3 The Statistical Group Comparison window Copyright 1998 2001 Silicon Genetics 4 4 Analyzing Data in GeneSpring Filter Genes Analysis Tools To Make a Statistical Group Comparison 1 Select the parameter on which you would like to base your comparison in the Parameter for comparison drop down list 2 Select the samples that you would like to compare by checking or unchecking the desired samples in the Select Groups to Compare box 3 Select the type of test that you would like to perform There are four testing options For details on the formulae used for these tests see Technical Details on the Statistical Group Comparison on page N 1 Parametric test assume variances equal checkbox filters based on the results of a Student s two sample t test for two groups or a one way analysis of variance ANOVA for multiple groups e Parametric test don t assume variances equal checkbox filters based on the results of an ANOVA or Welch s approximate t test for two groups This is the test that is most appropriate for standard experiments
364. rmation about the Gene Inspector To find more interesting genes repeat these steps The Find Interesting Genes command also automatically creates a list of interesting genes com plete with an interest score for each one in your Gene List folder In views where lists can be ordered such as the Ordered List view and Compare Genes to Genes view lists of interesting genes are ordered according to interest score in descending order For an example of Ordered List view please refer to Ordered List View on page 3 21 Copyright 1998 2001 Silicon Genetics 4 21 Analyzing Data in GeneSpring Making Lists from Selected Genes Making Lists from Selected Genes This command allows you to make lists from genes you select graphically To make a list from selected genes There are two ways to select a set of genes If genes are grouped together in the browser you can select a set in the same way you select an area to enlarge 1 While holding down the shift key click and drag a rectangle across the region you wish to select 2 Release the cursor while continuing to hold down the shift key Selected genes will appear in white Or e Select multiple genes by clicking over their representative lines or rectangles while holding down the shift key Once you have selected all the genes you want in your new list right click in the genome browser and selectMake List from Selected Genes from the pop up menu A New Gene List window wil
365. rols for your experiment In other words you want to normalize all of your samples to the arithmetic mean of a set of controls After Normalizing Each Sample to Itself Treated Samples Controls Gene Name Sp 1 Sp 2 Sp 3 Sp 4 Sp 5 Sp 6 CLN 1 1 1 3 1 1 1 CLN2 1 1 1 0 5 1 1 5 CDC28 0 1 0 1 0 1 0 1 0 1 0 1 HSL1 2 2 2 0 5 0 5 5 YGP1 10 10 10 10 10 10 Appendix G 14 Copyright 1998 2001 Silicon Genetics Normalizing Options Region Normalization After normalizing each sample to itself the samples are normalized to samples to the average of the controls Note that this allows you to analyze the variability among the controls as well as the treated samples After Normalizing All Samples to the Average of the Controls Treated Samples Controls Gene Name Sp 1 Sp 2 Sp 3 Sp 4 Sp 5 Sp 6 CLN 1 l 1 3 l l l CLN2 l l 1 0 5 1 1 5 CDC28 l l l l l l HSL1 l l 1 25 25 2 5 YGPI1 l l 1 l l l See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring Region Normalization This normalization option allows you to normalize sections of a sample rather than normalizing over the entire sample This is especially important if you used multiple arrays for each experi mental point or if there is some reason you need to normalize sections of an array separately from one another Region
366. rporate sequence data not included within a GenBank or EMBL file sequence the name of a file containing the sequence s for the genome sequence ecoli seq 6 Ifyou are using a Master Gene Table to define your genome indicate which format you used The four Master Gene Table format options are name list name function SGD or mapped These are also the four possible object values for this question See What Format do these Data Need to be in on page H 1 for a description of these formats This line is required if the ORFs line from question two was used ORFFormat the format for the Master Gene Table specified in the ORFs line ORFFormat mapped 7 Ifyou are using a supplementary table of genes file indicate which table of genes format is used in this file This can be one of the four table of genes format options name list name function SGD or mapped These are also the four possible object values for this question See What Format do these Data Need to be in on page H 1 for a description of these for mats This line is required if the nonORFs line from question four was used and the format for this file is different from the format given in response to question six nonORFFormat the format for the file specified in the nonORFs if different from the file of ORFs nonORFFormat name function 8 Ifthe genome you are entering has been sequenced then you should answer true to this question This li
367. rray Layouts Drawn Genes External Programs Bookmarks Scripts THE NAVIGATOR ALLOWS YOU TO SELECT THE DATA YOU CHOOSE TO WORK WITH NUS File Edit View Experiments Colorbar YOU CAN DRAG THE SLIDER TO MOVE TO DIFFERENT Genes all genes Tools Annotations Window Help 5 04Notmalized Intensity ratio OME BROWSER ALLOWS YOU TO YOUR DATA AND A ALYSIS f WA M IN vooo ox mMm y hey A Ah time minutes CURRE CHE 60 80 100 0 20 40 time 0 minutes lt n Animate THIS AREA SHOWS EXPE NT PARAME TER VALUES AT VARIOUS POINTS WITHIN AN EXPERIMENT IT ALSO LISTS THE MAGNIFI CATION LEVEL Magnification A Zoom Out POINTS WITHIN YOUR EXPERIMENT Figure 1 3 The main GeneSpring window Below are some basics to get you moving around GeneSpring Copyright 1998 2001 Silicon Genetics Changing the genes displayed Open the gene list folder in the navigator GeneSpring ini tially displays the all genes list You can change the genes shown in the display by choosing another list Views You can change the view in the genome browser using the View menu GeneSpring initially displays the Classification view where genes are displayed according to pre defined categories However you can view displayed genes as a graph a scatter plot a bar graph an Introduction GeneSpring Basics ordered list etc Note that some views such as Tree Pathway and Array Layout
368. rs would then be typically used to distinguish these points Typical examples are the same as for non continuous parameters This may be referred to as category Continuous Parameter is a numerical parameter for which interpolation makes sense Graphs using this parameter are line graphs If there are no continuous parameters in an experiment then histograms will be shown instead of line graphs A typical example of a continuous parameter is time or drug concentration Continuous parameters can optionally be made loga rithmic for display purposes Non continuous Parameter is a possibly numerical parameter for which drawing lines between points does not make sense but you still wish to graph it along the horizontal axis Typical examples of such parameters are drug type strain of the organism under study or tis sue type GeneSpring will typically display smaller graphs side by side in the genome browser This may also be referred to as discrete Replicate is not interpreted by GeneSpring Instead it is considered a tracking identifier Sub experiments that have all parameters other than the Replicate parameter the same are con sidered repeats These are visually represented on graphs by taking the median of the data val ues and plotting error bars Typical examples of such parameters are database identifiers and individual organism names Picture Copyright 1998 2001 Silicon Genetics Appendix Q 3 Glossary Pop up Menu A
369. rtant because this is where all the information about your genomes and experiments is stored Each genome or organism folder contains two key files the genome definition file genomedef and the master table of genes txt along with folders containing information relating to experiments maps trees gene lists and other data relevant to the particu lar organism Copyright 1998 2001 Silicon Genetics 1 16 Introduction Commonly Used GeneSpring Functions Commonly Used GeneSpring Functions To open a different genome choose File gt New Genome To open another copy of the main window choose File gt New Linked Window Each of these will bring up a new main win dow similar to the one described in GeneSpring Basics on page 1 7 To change preferences colors start up genome etc choose Edit gt Preferences See Appendix B Preferences Window for more details The Gene Inspector window Double clicking a gene will bring up the Gene Inspector window This window contains specific information about the selected gene See Gene Inspector on page 3 37 for details Information presented in the Gene Inspector might include e knowledge you have about your selected gene typically text e graphs of the selected gene s expression profile from the current experiment e links to internet or intranet databases on the web for the selected gene Making Lists There are many ways to create a list of genes see Chapter
370. s Conditions are groupings of one or more samples Each sample may be a condition as in the All Samples interpretation or a condition may include multiple samples For example because the experiment above is organized according to the parameter values Embryonic Postnatal and Adult these can be called the conditions the experiment Within these condi tions the parameter day is being treated as a replicate and has been averaged for each condi tion Embryonic Postnatal and Adult across all samples Hence a condition can include data from more than one sample Copyright 1998 2001 Silicon Genetics 1 14 Introduction GeneSpring Basics C Any gene trees created in GeneSpring are kept in the Gene Trees folder Gene trees are dendrograms used as a method of showing relationships between the expression levels of genes over a series of conditions D Experiment trees are like gene trees except that instead of showing the relationships between genes they show the relationships between the expression levels of samples Experi ment trees are kept in the Experiment Trees folder E The Classifications folder contains genes that have been grouped or classified to divisions defined by k means or SOM clustering F Pathways are images of regulatory or metabolic pathways that can be imported into Gene Spring Genes are overlaid on these images allowing you to observe their changing expression levels across experimental conditions A feature c
371. s DNA from a different genome than the one you are investigating on the array Entering true as the object value of the line given below means you have negative controls and you want GeneSpring to normalize your samples using the negative control val ues This normalization method takes the average signal intensities for all of the negative con trols and subtracts this number from the signal intensity of each gene For more info about this normalization option see Normalizing Options on page G 1 If you do not have negative controls or do not want to normalize your samples using the data from them either do not enter the NormalizeNegControl line or type false as the object value NormalizeNegControl either true or false NormalizeNegControl false Appendix J 14 Copyright 1998 2001 Silicon Genetics Installing from a Text File Normalizations Control Channel Values The required layout file for negative controls 20 If you do not have negative controls or are not using them to normalize your data skip this question and the associated experiment file entry If you are using negative controls you must have a layout file See The Layout file on page K 2 for what this file can or should con tain There are two normalization options requiring you to have a layout file They both use this line to tell GeneSpring where to find the layout file You should only have one layout file and you should only ent
372. s Menu P 4 Translate 4 31 translation table 4 31 Tree View 3 17 Trees comparing genes in nodes 3 18 labels 3 18 Minimum Distance 5 3 Separation ratio 5 3 viewing 3 17 troubleshooting Java Virtual Memory 1 2 Trust 3 32 t test 3 39 Tutorial A 1 two color experiments 3 32 Two sided Spearman Confidence 4 17 L 3 U under expressed color changing B 2 Update annotations 2 15 Update genes see GeneSpider Update GeneSpring A 2 upload to GeNet 6 7 Upregulated Color B 2 Upregulated correlation 4 16 L 5 Use list as Classification 3 27 Vv Venn Diagram 3 33 Version Notes A 1 vertical axis P 6 Vertical Label P 6 view gene details 3 37 View Menu P 3 Array Layout 3 22 Bar Graph 3 8 Classification 3 9 Compare Genes to Genes 3 24 Graph 3 7 Graph by Genes 3 26 Pathway 3 23 Physical Position 3 10 Scatter Plot 3 15 Copyright 1998 2001 Silicon Genetics W Web Connections 3 40 web databases C 4 special character C 4 Welcome panel D 3 Wizard Panels Y Array Photos D 12 changing panels manually D 3 Control Channel Values D 11 D 13 Data Column Location D 10 Data File Format D 4 Data File Header Lines D 8 Describe your Data Files D 6 Finish D 16 Flags D 11 Gene Name D 9 Gene Name Prefix Removal D 9 Gene Name Suffix Removal D 10 Graphics Specifications D 15 How to Display the Parameters D 5 Normalizations by All Samples to a Specif ic Sample D 15 Normalizations by Each Gene to Itself D 15 Normalizations b
373. s URL may contain many file formats Make certain to download the file with the suffix gbk An EMBL file may be used in place of a Gen Bank file Adding Extra Genes to a Genome Defined by a GenBank or EMBL file You can use a GenBank or EMBL file to describe a genome and add in some extra genes This is typically done to represent a strain slightly different from the sequenced strain To do this you need to create a separate Master Gene Table containing all of the extra genes you wish to add This file should be formatted using one of the four table of genes formats discussed in What For mat do these Data Need to be in on page 1 If you are using an original gbk file you can simply go to their web site and update the entire file Make sure you save it with the same name and to the same place as your current gbk file Appendix H 4 Copyright 1998 2001 Silicon Genetics Creating Folders for New Genomes Raw Data To update GenBank information 1 In GeneSpring open the genome you wish to update a GotoFile gt New Genome or Array Another menu appears The genomes included in this submenu depend on what genomes have been loaded into your copy of GeneSpring b Select the name of the genome you wish to update Goto Tools gt GeneSpider gt Update genes from GenBank Click the arrow to the right of the box labeled What the spider will use to mine GenBank A drop down menu will appear Click the name of the column in the
374. s from top to bottom in the colorbar Please refer Copyright 1998 2001 Silicon Genetics 2 13 Creating DataObjects in GeneSpring Definitions of Parameters to Color by Parameter on page 3 33 for details Parameter Values are listed in alphabetic or numerical order Each color represents a category or set of categories When coloring the browser display by parameter each parameter value defined as a condition is assigned a color and every data point described by that parameter is drawn in that parameter s color This can be referred to as Color by Parameter Using this parameter display option means the browser display shows the same gene multiple times the number of times a single gene is drawn is equal to the number of parameter values defined as conditions When the browser display is colored using a color option other than Color by parameter it is impossible to visually distinguish which parameter value a particular gene line or gene point represents although separate gene lines for each parameter value defined as a condition are still drawn Please refer to Re order the Parameters on page 2 10 for details on how to change that order Individual patients or strain types are variables commonly defined as color codes conditions because although they are different parameter values it is interesting to see them visually compared to one another It is likely the expression patterns of individual patients with the same disease are
375. s no information available on the variability of that condition References Rocke D M and S Lorenzato 1995 A two component model for measurement error in analyti cal chemistry Technometrics 37 176 184 Milliken G A and Johnson D E 1984 Analysis of Messy Data Volume 1 Designed Experi ments Wadsworth Inc Belmont California Box G E P Hunter W G and Hunter J S 1978 Statistics for Experimenters John Wiley and Sons New York Satterthwaite F E 1946 An approximate distribution of estimates of variance components Bio metrics Bulletin 2 110 14 Copyright 1998 2001 Silicon Genetics 2 29 Creating DataObjects in GeneSpring Global Error Models Copyright 1998 2001 Silicon Genetics 2 30 Viewing Data in GeneSpring Using Genome Browser Chapter 3 Viewing Data in GeneSpring Using Genome Browser The large panel in the center of the GeneSpring window is the genome browser which graphically displays information about the genes in the selected gene list The genome browser often presents so much information that individual genes and gene names are not visible To look more closely at fewer genes you can zoom in and pan around Zooming In You can enlarge a region of the screen by zooming in 1 Click and drag a rectangle across the region you wish to enlarge 2 Release the cursor Repeat steps 1 and 2 until you reach the desired magnification level 3 To undo a zoom type Ctr1 Z
376. s or SOM Coloring a Previously Saved Classification You can use a previously saved classification for coloring 1 Open the Classifications folder by clicking its icon 2 Select a classification by right clicking over the name 3 Select Set as coloring scheme from the pop up menu and GeneSpring will automat ically update to reflect the new coloring scheme Copyright 1998 2001 Silicon Genetics 3 34 Viewing Data in GeneSpring Changing the Coloring Scheme The colorbar will show the names of the sets present in the chosen classification gt GeneSpring 4 1 Yeast Genes all genes E Elojxi Fie Edit View Experiments Colorbar Tools Annotations Window Help H Gene Lists _ Gene Ontology set4 c Yy T sets like YMR199W CLN e Experiments t EHL Gene Trees i set2 Experiment Trees m Classifications i5 cluster K Means fi set3 Chromosome Num e Pathways i Array Layouts e sett Drawn Genes SEET time minutes External Programs 5 0 Aormalizad unclassified 1 034 genes Bookmarks 3 0 Intensity ratio n Unciassi Scripts time minutes 9 time 0 minutes Animate Magnification 1 Zoom Out nim Figure 3 17 A Split Window colored by Classification Split Window and Color by Classification You can also use the Split Window feature with the Color by Classification scheme 1 Select a gene list to view 2 Right click over a folder or a previously saved classification and se
377. sam ple s trust is calculated as follows the median value of the chip x the average of the gene s measurement in control samples GeneSpring automatically interprets trust for Affymetrix data specifying 500 as data that is most trustworthy 150 as moderately trustworthy and 50 as least trustworthy For other data you will need to enter these numbers manually Consult the manuals for your array scanning software or hardware and estimate these trust levels based on the detection limit and noise levels for any given measurement To set the trust interpretation 1 Right click the colorbar 2 Click Set Range 3 Enter values for High Control Strength Medium Control Strength and Low Control Strength 4 Click OK Copyright 1998 2001 Silicon Genetics 3 32 Viewing Data in GeneSpring Changing the Coloring Scheme Color by Significance Data is colored based on how far the gene is over or underexpressed relative to a normalized expression level of 1 in terms of the standard error of the measurement The standard colorbar is replaced with a colorbar ranging from 30 to 30 The standard error model is based on the Glo bal Error Model if the Global Error Model is turned on For more information about the Global Error Model see Global Error Models on page 2 26 Otherwise the standard error is based on the standard deviation of the replicate data for a particular gene and condition for information about the calculation of t
378. second number 2 Number inputs Output is a number Number Mul Multiply two numbers together 2 Number inputs Output is a number Copyright 1998 2001 Silicon Genetics 4 39 Analyzing Data in GeneSpring External Programs e Number Sub Subtract the second number from the first number 2 Number inputs Out put is a number 11 Promoter e Find Genes in GeneList with Regulatory Sequence Produces a Gene List showing the genes that contain the input regulatory Sequence 1 Sequence input amp 1 Gene List input Knobs for From Base To Base amp Maximum errors Output is a Gene List e Find Genes with Regulatory Sequence Produces a Gene List showing the genes that contain the input regulatory Sequence 1 Sequence input Knobs for From Base To Base amp Maximum errors Output is a Gene List e Find Regulatory Sequence Find regulatory sequences upstream of the genes in the Gene List specified as input 1 Gene List input Knobs for From Base To Base Minimum Length Maximum Length Minimum Errors Maximum Errors Minimum Interior N s Maximum Interior N s Relative Genomic p value cutoff Output is a Group of Sequences Auto Publish to GeNet You can also use Scripts to automate publishing to GeNet External Programs GeneSpring External Program Interface The GeneSpring External Program interface allows you to run external analysis programs from within GeneSpring These programs can be useful when your resea
379. sent in a region The Positive and Negative Control Files A positive control file and a negative control file are formatted in exactly the same way their con tents are different Each file lists the control genes names one name per line Control Gene Name 1 Control Gene Name Control Gene Name Control Gene Name Control Gene Name Control Gene Name ou WN This list of gene names is all either file should contain There should not be any headlines or any thing else in the file only the gene names Briefly you have negative controls in your experiment when there is DNA from a different genome than the one you are investigating on the array You are using positive controls when there is DNA from a different genome than the one you are investigating on your array and you add a known quantity of that different DNA to your sample For a description of the possible nor malizations to be done with these controls see Normalizing Options on page G 1 The names of the positive and negative controls do not need to be listed in your Master Table of Genes If they are listed those genes will be colored gray not measured in the genome browser because they are used in normalization not measurement Appendix K 7 Copyright 1998 2001 Silicon Genetics Experiment File Formats Where do I put my data Where do I put my data There are eight possible raw data files listed below only the first one is necessary for loading an experimen
380. separately but can occasionally be used in combination with each other or with the standard way to designate a region 1 The regions are defined implicitly by the order the genes names as reported in the experimen tal data file The names of the genes can be sorted in alphabetical order and used to determine whether a gene is in this region One can specify inclusive beginning and ending genes and any genes between them alphabetically will be considered part of this region See the next option for the meaning of UsesCommas EndRegion the last gene name in the region StartRegion the first gene name in the region UsesCommas false EndRegion s191 StartRegion s001 UsesCommas false 2 The regions are defined implicitly by the ordered names of the genes in a rectangular coordi nate system This is similar to the previous option except the names of the genes are actu ally coordinates separated by commas In this case a gene is only in the given region if it is between the starting and ending gene names for each dimension separated by commas For instance StartRegion 001 100 EndRegion 099 199 UsesCommas true 3 The regions are defined explicitly by a list of gene names and optionally a change of names In this case you must define a map for the region A map can be just a list of genes or it can be a list of names as used in the experiment files and the corresponding gene names as used in gene list d
381. served If you have no replicates condition and sample can be considered syn onymous 1 2 3 4 5 Open the Experiment folder in the navigator by clicking on its icon Click the sign next to the experiment icon Click the sign next to the interpretation icon Right click over a condition Select Inspect from the pop up menu Copyright 1998 2001 Silicon Genetics 3 43 Viewing Data in GeneSpring The Inspectors Condition Inspector B lol x Parameters time minutes 10 Similar Conditions Correlation Condition 0 91242 time 20 minutes 0 89548 time 30 minutes 0 88863 time 0 minutes 0 86759 time 40 minutes 0 86283 time 50 minutes 0 86092 time 80 minutes 0 851 time 60 minutes 0 8483 time 70 minutes 0 84504 time 140 minutes 0 84441 time 120 minutes 0 83899 time 130 minutes 0 83772 time 110 minutes o Figure 3 21 The Condition Inspector window The Parameters Box In this box is a brief description of the sample currently under inspection The Similar Conditions Box e Correlation This list of numbers is how closely correlated the other conditions in the exper iment are to the one under inspection The conditions are listed in the order from most closely correlating to least correlating e Condition This is a list of the other conditions in this experiment briefly described Dou ble clicking any one of them will bring up a Condition Inspector for that condition Li
382. sesssee 2 1 The Experiment Autoloaders 2c5 ccssevdtiaws astins Aateo tac i Ae nian 2 1 Autoloader Normalizations s s js0sicessdeacsesdcassaednasoaged cxssthaveastherdiaataasearsaesducsdateciast 2 3 Default Normalizations of Commercially Available Products cc cece 2 4 Merging Splitting and Duplicating Experiments 2 0 0 0 eeeceseceseeneeeseeneeeeeeeeeeeenaes 2 6 Loading from Subchips gy cose ccc etaandccegrecstesteucksacaren ys deasswaanceNttcnanenenreue wera 2 7 Creating a Genome through the Autoloader 0 cceccceccccceseceseeeeeeeeseecneeeeeeeneeeenneees 2 7 Change Experiment Parameters cdcsigeiesaicvisoscdccdallatin aes oe evaliais weses cise pacdintietads vistas 2 8 The Experiment Parameters Window saisscasdis cmvenvau diese taveaictnrensssss 2 9 Adda Param ter sie eaten a area eran oad See aa tala 2 10 Re order the Parameters acct curbs sc nacs sarees cadeles aped caren couaseeecesacsaemectieapelaneanmeecanoaes 2 10 Definitions OF Parameters uoiuncsc cae nuke cists sad bedeces an aute nn a a aioa 2 11 Parameter Vocab lary cccissazenscytise as bas bachansiacgsdaasaze A a AAT a E ARRATSA 2 11 Parameters Displayed in the Navigator essssssessssessessrssrosseessssresseessesersseesse 2 11 A Note on Multiple Parameters 5 ccsvauieeatiscssecidesoadioerstedectedalnaae eid eats 2 12 Parameter Display Options sesesssssessseesseseesseesseserssessessrssresressessrsseesseeseesressee 2 12 Continuous Elementari n s e E E eA A
383. sest to your selected point on the diagram and list B contains all other genes on the pathway GeneSpring then examines all the genes on your currently selected gene list and finds all genes whose minimum similarity correlation with genes on list A is higher than their maximum simi larity with genes on list B These genes are made into a separate list for you to examine You can place a gene from this list on the pathway see Adding a Gene to a Pathway on page 4 24 Note that if your pathway geometry is complex this procedure will not be particularly useful as it relies on screen distance only not pathway structure or connectivity To Find New Genes on a Pathway 1 Right click near a group of genes displayed on your pathway 2 Choose the option Find Genes Which Could Fit Here The New Gene List window will appear 3 Enter a name and folder for your gene list and click Save Your new gene list will be saved in your Gene Lists folder Pathway Commands Right click your Pathway in the navigator for the following options e Display Pathway Displays the selected pathway in the genome browser e Properties Brings up the Properties box listing such details as pathway history and genome e Attachments Allows you to add a text or picture attachment to your Pathway Copyright 1998 2001 Silicon Genetics 4 25 Analyzing Data in GeneSpring Regulatory Sequences Make Gene List Allows you to save a list of all the genes on the select
384. ssessessressessrssees G 18 Normalization for Particular Array Types sssesessseessessseseessessresresseesesresseeseesressee G 18 Appendix H Creating Folders for New Genomes sessseessooesoossosesssoesooesoossosssssesssoee H 1 RaW D tava enerne a n a ah e a a ea a ea a a ei H 1 What Data Are Necessary accnsnioscone n ios a i E E R R H 1 What Format do these Data Need to be in sssnsssosesessessesessessesreseosseesessresseesese H 1 Appendix I Installing a Genome from a Text File cccsscscssccscsecsscsssccssesceseees I 1 Creating Folders for New Genomes gt acc geicsscclijadelscshinccs aeetbassuaeaceuadeenncaensea muah I 1 The genomedef Fillerin eked oa ah ata iad site aac ih ato adele a aes I 1 Define Your GENOE se ola a See state oan ns a 2 Appendix J Installing from a Text Fille cscccscsscccsscccsscccssccsssscccssssccsssessessees J 1 Define Y our Experiment eer niron Aan a enna Aes ea Ae een Aaa J 1 Define Yo r Parameters aneirin iisen audapaleyaavdians EAE aa a aa ia iaei J 2 Describe VOUT Data FMS ida ca estes singaeathauaegud asso tcaaa dina e e a a A e EE E J 6 Data File Header Les eset 4 salsa n aaah Faas A A Eat J 7 Gene NaMES sinaia cures sranna Esna AS iaasa a ae a anina aeea aria aaa dat taiii J 8 Explain to GeneSpring how to locate only the Gene Name ssssssssessesessesseeeesseseesesse J 8 Explain to GeneSpring How to Read the Region Specifications 0 ccs eeeeeeeeeeeeeee J 9 The required
385. ssigned the average of the ranks e g if the Sth 6th and 7th lowest values are tied all three datapoints are assigned a rank of 6 This is how to compute a Spearman correlation Order all the elements of vector a Use this order to assign a rank to each element of a Make a new vector a where the i element in a is the rank of a in a Now make a vector A from a in the same way as A was made from a in the Pearson Correlation Similarly make a vector B from b Spearman correlation A B A B Spearman Confidence Spearman confidence is a measure of similarity not a correlation Spearman confidence is one minus the p value for the statistical test when the Spearman correlation is zero versus the alterna tive when it is larger than zero There is a high Spearman confidence value if there is a high Spearman correlation and a low p value meaning there is a low probability to find a correlation this high This measure is very similar to looking for large Spearman correlation values but it takes account of the number of sub experiments in your experiment set This is how to compute a Spearman confidence Ifr is the value of the Spearman correlation as described in Spearman Correlation on page 3 then Spearman confidence 1 probability you would get a value of r or higher by chance Two sided Spearman Confidence Two sided Spearman confidence is again a measure of similarity but not a correlation It is very similar to
386. ssion level between groups of samples This restriction will remove genes based on the mean normalized expression levels of a group according to your current interpretation mode log arithm ratio or fold change You will need to specify which parameter is to be used for the com 4 3 Copyright 1998 2001 Silicon Genetics Analyzing Data in GeneSpring Filter Genes Analysis Tools parison the particular statistical test to be performed and the cutoff on the p value to be used in identifying statistically significant results For example you can use the Statistical Group Comparison feature to filter out genes that do not vary significantly across different groups with multiple samples This allows you to find those genes that exhibit important changes between various conditions of the experiment This compar ison is performed for each gene and the genes with sufficiently small p values are returned O x Statistical Group Comparison Random Data time series This will find genes that show statistically significant differences in the mean expression levels across groups defined by distinct levels of the comparison parameter Statistical Comparison Constraints Parameter for comparison time minutes Select Groups to Compare Select All TestType Parametric test assume variances equal Student s ttest ANOVA Parametric test dont assume variances equal Welch ttest Welch ANOVA Paramet
387. sssessseeseeseessesseserssresseserse A 1 Ehe Hel VA SIMU r ena e os kee a a a a a A A 1 GeneSpring Basics Instructional Manual s sseseesessseessesesssesseserssressessessressesse A 1 Mantalite cepa a e a aa e lea a a e aea A 1 FAQ raae AEE E EE EE AE aE A A 1 Version Notes css lt a5czrascnsdausdandamcsdctdvzantayad n aeaa a EEA aa Ea T Ean aia aa A 1 Upd te CIES S PHI O seatcmsovet atcsieh eae cuca cua bsedue ie e a E ma ESES A 2 Silicon Genetics on the W Cis cs cstarag os viata helt ea a a ea gatas A 2 GENCE Database siasaxidvaseinssistanreataevicestandeaasebanvasieousbudsishontestagntinn demavunenciiaeeunig A 2 Register for a Workshop siccsscscctsaveetongzandectushivieay toedcdtsange eugeiniecatetahocasteetasis A 2 SV SUSHIL ON surede nin a r a a eaa a ai A 2 A DO a r e eee A e e N e EA A daccen lade ar A 2 4 Copyright 2000 2001 Silicon Genetics Appendix B Preferences Window ccscccsssccccssccsssccccsssccssssccssssesesssescessesessescesseees B 1 Data PUGS ascssseadacsareossanapasdanstenstancusaesayetica sain shasteseadslangeBiasiator eas heed idataadesaasegbadsiatoanes B 1 Database cete is container na ec SecSiva E dees e eens eae ae Raa eo B 1 Colorno ee Re ee Ne re ee ee ea hire Serre utente B 2 Specific Golo Definition i reie ea a e Seed E tes was are eee B 3 Gene Labelen naan e REE OPES E a a me anne Creer B 4 Browser Details nesr oseigan drene naii a EE E E E A n eas B 4 The Firewall Details bok ensrnnnnesn
388. st name function SGD and Mapped The reason these formats are called Master Gene Table is because it is easiest to create them in spreadsheet programs such as Microsoft Excel and then use the Save As com mand to create tab delineated text files Occasionally a Master Gene Table is referred to as the Table of Genes the Master Gene List or the Array Element List Name List The simplest format for a Master Gene Table is name list In this format the Master Gene Table is a single column comprised of the names of the genes Genel Gene2 Gene3 Appendix H 1 Copyright 1998 2001 Silicon Genetics Creating Folders for New Genomes Raw Data Gene names with spaces in them such as Gene 1 are acceptable Name Function The next simplest format for the Master Gene Table is name function In this format the table of genes is the same as the table for name list except each gene may be followed by a descrip tion of its function If you have additional information about the genes enter it in the same row as the gene it refers to separated from the gene name by a tab character or column separator in Microsoft Excel An example of this is Genel Putative Phosphokinase Gene2 Gene3 Deletion causes 2 tails You do not need to have information about every gene In the example nothing is known about Gene2 so the line after its name is left blank If you have a list of genes and text information about
389. st Inspector You can view the contents of a gene list and the method with which it was created using the Gene List Inspector The Gene List Inspector is especially useful in learning about lists that have been identified using the Similar List function The Gene List Inspector shows the history of your gene list a graph of your list a table of all the genes included in the list and a collection of gene lists that are statistically similar to your gene list The history of the gene list is in the upper left corner of the window You can change this informa tion with the Edit button In the upper right corner of the window is a browser graphing your list Right clicking on this browser gives you several options see Using Genome Browser on page 3 1 for information on browser options In the center of the Gene List Inspector window is a table of all the genes included in the list Double clicking a gene or cell in this table brings up a Gene Inspector window for the selected gene see Gene Inspector on page 3 37 for information Copyright 1998 2001 Silicon Genetics 3 44 Viewing Data in GeneSpring The Inspectors on the Gene Inspector The Similar Lists box in the lower left of the window contains names of lists resembling the displayed list or containing a statistically significant number of overlapping genes The statistical significance is listed as the p value for each of the similar lists You can right click on one of these lists
390. stant value see hard number continuous parameter J 3 Control Channel Background Column D 11 J 11 Control Channel Values D 11 J 11 J 15 minimum value J 15 pre normalized data J 16 Copy lists to clipboard 3 46 Copying and Pasting data F 1 correlation weighted 5 2 5 11 Correlation commands 4 14 L 2 Correlation Equations Change correlation 4 16 L 5 Distance 4 17 L 4 Pearson correlation 4 17 L 2 Smooth correlation 4 16 L 4 Spearman Confidence 4 17 L 3 Spearman correlation 4 17 L 3 Standard correlation 4 16 L 2 Two sided Spearman Confidence 4 17 L 3 Upregulated correlation 4 16 L 5 D Data Column Location D 10 J 9 data directory H 6 K 8 Data File Format D 4 Data File Header Lines D 8 J 7 Data Import Wizard Experiment D 3 Genome C 1 data location K 8 data objects 6 6 Database E 1 JDBC driver B 1 Index 1 DBMS E 1 dendrogram see Tree View Describe your Data Files D 6 J 6 Display Parameters J 2 Distance 4 17 L 4 Downregulated Color B 2 E Each Gene to Itself J 18 minimum average J 18 Each Sample to Itself J 17 minimum average J 17 EC Number H 2 Edit Menu P 2 equations overall correlation 5 3 Error bars P 7 Euclidian metric L 4 Experiment Inspector 3 41 buttons 3 43 interpretations 3 42 normalizations 3 42 notes 3 42 parameters 3 42 experiment installation files K 1 experiment interpretation changing 2 17 Fold change 2 19 log ratio 2 18 vertical axis 2 18 Experiment Name J 1 P 6 ex
391. t You must have e Experimental data file s containing the genes raw data for each sample in the experiment Please refer to Raw Data on page 1 You might have e A Layout file e Region designation file s e A map file e A file listing the positive controls e A file listing the negative controls e GIF or JPEG pictures of the conditions during the experiment e GIF or JPEG pictures of the Microarray plates the experiment was done on All of the raw data files should all be placed within the Experiment sub folder of the organism they pertain to The default pathway for this directory is C Silicon Genetics GeneSpring Data Genome Name Experiments If the defaults were changed your version of GeneSpring may be stored elsewhere but the end of the pathway should be identical on your computer Appendix K 8 Copyright 1998 2001 Silicon Genetics Equations for Correlations and other Similarity Measures Appendix L Equations for Correlations and other Similarity Measures Many of the advanced analysis technics are based upon measures of gene similarity Similarity or nearness between genes is usually based on the correlation between the expression profiles of the two genes GeneSpring offers nine choices of similarity measures Each is selectable from a drop down list appearing the Clustering and Filtering windows Please refer to Chapter 5 Cluster ing and Characterizing Data in GeneSpring and Filter G
392. t your experiment when you click OK The Interpretations Box A list of all the interpretations associated with this experiment is in the Interpretations section of the Experiment Inspector window You can select any of the interpretations in the white text boxes by clicking over them A double click will bring up the Change Experiment Interpretation win dow automatically If your computer is not set to acknowledge a double click select with a single click and select the Change button This will bring up the Change Experiment Interpretation window Please refer to Changing the Experiment Interpretation on page 2 17 for details on this window Any changes made in this window will be saved and affect your experiment when you click OK The Normalizations Box Near the bottom of the Experiment Inspector window is the Normalizations panel Here you can read what normalizations are currently being used in your experiment If you would like to use the text elsewhere you can click the Copy button and the text will be placed on your clipboard for use in other applications Selecting the Change button in the Normalizations box will result in the Experiment normaliza tions window Please refer to Experiment Normalizations on page 2 21 for details on this window Any changes made in this window will be saved and affect your experiment when you click OK Copyright 1998 2001 Silicon Genetics 3 42 Viewing Data in GeneSpring The Inspectors
393. table of genes containing the GenBank Accession Num bers Click the Start button The GeneSpider will process GenBank s data displaying how far it has gotten in the box labeled Status If you get a dialog box with an error you can click the close button on the upper right hand corner of the error messages and continue the operation Type the name of the text file you would like the new Master Gene Table saved as in the box labeled Save gene list to If you save the new Master Gene Table using the same name as the current table file in this example ORF _table txt then the updated file will define this genome rather than the previous table of genes file If you save this updated Master Gene Table under a different file name for example ORF_table2 txt then the old Master Gene Table will continue to define the genome although the updated Master Gene Table will have been saved in the same directory as the original Master Gene Table Click the Save and Close button to save the updated Master Gene Table If for some rea son you do not want to save close the window by clicking the close button the upper right hand corner You can select the Save and Close button at any time during the update The searched items will have been temporarily stored in your computer and will be visible in GeneSpring when you restart It will go through the genes it has already updated really fast It will take five to 30 seconds per gene depending on how much data
394. tc These variables allow you to look for meaningful patterns in you data and deal sensibly with replicate experiments Appendix Q 4 Copyright 1998 2001 Silicon Genetics Index A adding extra genes H 4 affine background correction 2 23 G 18 All Samples to Specific Samples J 18 Animation Controls 3 6 API E 1 Array Element List see Master Gene Table Array Layout view 3 22 Array Photos D 12 Attachments P 7 B background signal J 10 Bar Graph view 3 8 browser display Picture 3 7 Build Simplified Ontology 2 16 C Calinski and Harabasz index 3 47 Change Coloration 3 31 Change correlation 4 16 L 5 Change Experiment Interpretation 2 17 change experiment name 3 42 Change Vertical Axis Range P 5 changing restrictions 4 9 Class Predictor 5 15 Classification Inspector 3 46 class 3 47 Classification view 3 9 3 27 CLI E 2 Cluster P 4 results 5 11 Cluster Menu see Tools Menu Clustering window similarity definitions L 1 Color by Classification 3 34 by Parameter 2 14 3 33 by Secondary Experiment 3 35 by Significance 3 33 by Venn Diagram 3 33 changing the defaults B 2 No Color 3 34 Trust 3 32 Copyright 1998 2001 Silicon Genetics Color by Primary Experiment see Color by Expression color code parameter J 3 Colorbar J 19 Common Name H 2 Compare Genes to Genes view 3 24 Interesting Genes 4 21 complementary bases show hide P 6 Complex Correlations 4 18 Condition Inspector 3 43 Conjectured Regulatory Sequence 4 29 con
395. te however that the Column Editor will not remember a format if you have more than one sample in a file or if you have more than one signal column GeneSpring will ask you to name your format 4 Click Load Now to load the experiment The Select Genome window will appear 5 Choose an existing genome or create a new one 6 Choose an experiment name and click Save Your experiment will appear in the genome browser After loading an experiment examine and change your normalizations interpretations and parameters e To change normalizations select Experiments gt Experiment Normaliza tions See Experiment Normalizations on page 2 21 for details e To change parameters select Experiments gt Change Experiment Parame ters See Change Experiment Parameters on page 2 8 for details e To change interpretations select Experiments gt Change Experiment Inter pretation See Changing the Experiment Interpretation on page 2 17 for details Autoloader Normalizations The Autoloader will normalize your new files based on the technology used to create the original data files For more information on normalizations see Experiment Normalizations on page 2 21 2 3 Copyright 1998 2001 Silicon Genetics Creating DataObjects in GeneSpring The Experiment Autoloader One Color Experiments One Color normalizations will automatically display all information flagged as Present or Unknown e Per chip Distribution of
396. ter true as the object value To ignore this ability thus leaving the gene name alone either enter false as the object value after RemoveSlash or do not include this line in your experiment file RemoveSlash either true or false RemoveSlash true Appendix J 8 Copyright 1998 2001 Silicon Genetics Installing from a Text File Explain to GeneSpring How to Read the Region Specifications Explain to GeneSpring How to Read the Region Specifica tions Skip these questions and their associated entries in the experiment file if the samples in your experiment did not involve multiple arrays or sections of arrays needing to be normalized sepa rately 15 If your experiment used multiple arrays or sections of arrays needing to be normalized sepa rately indicate to GeneSpring which column of your data file indicates the region of the array and or which array a particular gene reading came from RegionColumn number of the column the region specification is found in RegionColumn 1 If your data files all have a different column layout but all of them have the region specification in the same column you may use the general object name given above rather than entering the column number of the region specification for each data file If you have more than one data file with different column layouts and they have different columns containing the region specifica tion use the object name given below If yo
397. ter Normalizing to a Control Channel Value for Each Gene Gene Name Sample 1 Sample 2 Sample 3 CLN 1 l l 3 CLN2 1 1 1 CDC28 l l 1 HSL1 1 1 1 YGP1 1 1 1 See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring Appendix G 4 Copyright 1998 2001 Silicon Genetics Normalizing Options Normalize to Positive Controls Normalize to Positive Controls This normalization method is intended to remove the differences in amount of exposure between samples providing you with a baseline so different samples are comparable to one another Posi tive controls give you a general idea of how well the array responded to exposure Normalizing to positive controls will factor in this information with the experimental results you analyze You can normalize your data with this method if you have genes designated as positive controls on your array you usually have positive controls when there is DNA from a different genome than the one you are investigating on your array and you added a known quantity of that DNA to your sam ple The formula used to do this is the signal strength of gene A in sample X the median signal of the positive controls in sample X This normalization should not be used with normalizing each sample to itself as they are both intended to address the same issue After normalizing to positive controls you probably still want to normalize each gene to itself
398. tering your Prepared Database into GeneSpring on page E 5 The only difference is you enter experi ment identifiers instead of file names and SQL table columns instead of tab delineated column headers Parameters describe what the database knows about each sample Different databases have differ ent ways of storing parameters so they must be retrieved by explicit SQL statements Silicon Genetics can provide these for GATC and help write these for individual databases This only needs to be done once Afterwards the customer simply chooses the database and GeneSpring will get data from it Normalization and other options can also be set for a database Adding an Experiment from a Database Make sure you have a database Any database software can be used to produce a database First you must make sure that GeneSpring will be able to see your database Your database s creator should have done this already If they have you can skip down to Connect your Database to GeneSpring on page E 4 1 Go to the control panel of your computer 2 Select ODBC Data Sources A new window The OCBC Data Source Administrator will come up To make a new ODBC source 1 Go to the system DSN 2 Click Add which will bring up a new Create New Data Source window 3 Select the correct type of database from the scrollable list This will bring up a new panel 4 Give the experiment a name This is the name GeneSpring will use so please remember t
399. the browser and toggling Hide experiment name from the Options submenu e Graph raw data Graph normalized data You can display raw or normalized data as shown in the upper right corner of the Gene Inspector window by right clicking in the browser and toggling Graph raw data from the Options submenu Appendix P 6 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Navigator The Error Bars Submenu Before you turn the error bars on go to Experiments gt Change Experiment Inter pretation and select the Use Global Error Model checkbox Please refer to Global Error Models on page 2 26 and Global Error Models Technical Details on page N 1 for more details and restrictions on this topic e Show Error Bars Hide Error Bars You can show or hide error bars by right clicking in the genome browser and toggling Show error bars from the Options submenu Error bar will only show for averaged data if you cannot get error bars to show check your parameters or re define one as a replicate e Standard error bar This feature only works in the Graph view when the error bars are showing You can display the Standard deviation error bars by right clicking in the genome browser and toggling standard deviation error bar from the Options submenu This feature is not enabled in the Gene Inspector window See Common Commands in the Experiment Specification area on page 10 for more information e Standard deviation T
400. the genome browser You can also unsplit the window by selecting View gt Unsplit win dow The Gene List Subfolder or Gene List Pop up Menus A right click over a gene list will bring up the following commands e Display List The number of genes displayed in the genome browser can be limited by choos ing a gene list Creating gene lists can be done in a number of different ways For detailed descriptions of how to do this see Filter Genes Analysis Tools on page 4 1 The Gene Lists folder in the navigator lists all of the gene lists GeneSpring currently knows about This includes lists you have made and the list currently displayed in the genome browser There are some subfolders such as the PIR keywords The subfolders are marked with a plus sign next to their icons Clicking one of the proffered gene lists those with a DNA on a page icon selects that list to be displayed in the genome browser e Translate The options new in GeneSpring version 4 0 allows you to find genes in one genome that are also present in other genomes Please refer to Making Lists of Homologs and Orthologs on page 4 31 for more details on this feature e Display As Second List Depending on the view you are currently looking at this command may bring in a second list all colored in green e Venn Diagram This command allows you to assign various lists colors within a Venn Dia gram The submenu contains three options left right and bottom See
401. the 10th percentile is set equal to 0 The affine background cor rection is applied only when the 10th percentile is more negative than the median of the data is positive You will get a warning message when loading your data if the correction is applied Copyright 1998 2001 Silicon Genetics 2 23 Creating DataObjects in GeneSpring Per chip Normalizations Also in the Gene Inspector control strengths adjusted by this correction are flagged with aster isks To tell GeneSpring If and When to Apply the Affine Background Correction The Options pull down menu in the Experiment Normalization window allows you to do this e Use simple ratio Tells GeneSpring to never use the affine background correction If the con trol value is negative GeneSpring will produce a warning message and will not do the normal ization e Use ratio with background correction Tells GeneSpring to always use the affine correc tion You will only want to select this option if no background subtraction has been performed on your data as it forces the 10th percentile to be 0 as if it were considering 10 percent of the data background As nearly all image analysis software has already done background sub traction this should be a rarely used option e Use background correction if needed Tells GeneSpring to use the affine correction as needed to compensate for negative values Use Constant Values If you are using a technology that calculates its own number for nor
402. the Next button in the Genome Wizard to move to the next panel If you click Next without specifying your genome directory then GeneSpring will create a directory for you in the GeneSpring data directory Directories automatically created in this way are named using the name of your genome GeneSpring will automatically copy your files into this directory You can select File gt New Window to see the new files 3 The Overall Genome Properties panel will appear In this window you tell GeneSpring whether the genome you are entering has been sequenced and if it has a circular genome a In the first box select the Yes circle if your organism has been sequenced otherwise leave the No circle selected b In the second box select the Yes circle if your organism is a circular genome like bacte ria plasmids and viruses If it is GeneSpring will display it as a circle in the physical position display Leave the default setting of No selected if your organism does not have a circular genome c Click the Next button to move forward to the next panel 4 The GenBank Data File panel will appear While GenBank offers several different files for their complete genomes GeneSpring can only read their gbk files In this panel you tell Gene Spring if you are using a GenBank file as your data source and if so what the file is named An EMBL file may be used in place of a GenBank file For the purposes of this panel treat the EMBL file as if it wer
403. the area under the genome browser that indicates which if any sub experiments is being displayed e g a particular time point in a time series experiment Expression production of mRNA through transcription of a DNA gene sequence Expression level the amount of mRNA produced by a given gene under specific conditions External Program analysis programs outside GeneSpring which can be launched from within GeneSpring Data from GeneSpring is sent to the program and output from the program is rec ognized by GeneSpring These programs are kept in the External Programs folder F Folders the yellow icons denoting the various directories where data is stored e g Gene Lists folder Experiments folder etc G Gene List a list of genes based on some criteria Gene Tree dendrograms used as a method of showing relationships between the expression lev els of genes over a series of conditions Genome the set of all genes on a chip or array Genome Browser the area of a GeneSpring window containing a visual representation of genes I Interpretation Experiment Interpretations tell GeneSpring how to treat and display your experi ment parameters and how normalized values should be treated M Main Screen the first GeneSpring window that appears after you open a genome such as the default yeast genome window that appears after initially starting the program Measurement the smallest unit of data recognized by GeneSpring
404. the black genome browser A menu will appear 2 SelectOptions gt Load Sequence A window saying Please wait while nucleic acid sequence is loaded will appear After the loading is complete it is possible to zoom in and see the nucleic acid sequence of a particular gene The sequence will be shown in the magnified genes However this information is not saved so when you exit GeneSpring and re open you will need to reload the nucleic acid sequence If you would like the sequences to always be readily available you must change the defaults through the Preferences window You may choose to make the load sequence feature automat ically load with the program Again please note that this applies to version 4 0 and earlier Method 2 takes effect in your next GeneSpring session 1 SelectEdit gt Preferences The GeneSpring Preferences window will appear 2 Select Data Files from the drop down at the top of the window 3 Select the Load Sequence checkbox 4 Click the OK button at the bottom of the window 5 Close and restart GeneSpring Or you can select File gt New Window Changing the defaults in the Preferences window will not initiate the load sequence feature in your current session but it will change future initial loading practices The nucleic acid sequence can also be loaded as a side effect of using Tools gt Find Regulatory Sequences For more information on this particular feature see Regulatory Sequences on page 4 26
405. the different disease possibilities such as breast cancer kidney cancer liver cancer brain cancer hepatitis A hepatitis B osteoporosis arthritis syphilis and no disease you might want to use several experimental parameters for this experiment Using multiple parameters even if they all refer to the same information allows you to group the data in many different ways which may give you different insights into your data set Parameter Display Options GeneSpring offers four ways of visually displaying a parameter a continuous element a non con tinuous element a replicate or hidden element or a color code When you enter a new experi ment in the Experiment Wizard you will be asked which display option is most appropriate for each of your parameters Your chosen display option will become the default display for that parameter If you simply paste in a new experiment all the parameters will be assigned the contin uous display option Regardless of how a parameter is entered in GeneSpring you can change how each parameter is displayed within GeneSpring using the Experiment gt Change Experiment Interpretation command For more details on this see Changing the Experiment Interpretation on page 2 17 Copyright 1998 2001 Silicon Genetics 2 12 Creating DataObjects in GeneSpring Definitions of Parameters Replicate or Hidden Element Parameters defined as replicated are averaged together and appear as a single parameter A par
406. the ends of the lines GeneSpring 4 1 Rat Genes test Drawn Gene start high with secondary list interesting genes EG File Edit View Experiments Colorser Tools Annotations Window Help EH Gene Lists _ Classification _ Classification _ PCAtest 5 _ Product protein PN Ho ca 1 7 ETE Fore allgenes ST SEER SOD o r TT r TET TEETH ort S actin SSRI CNTFR ciliary neurotr HMO ee oc A I 0063 2 Bo test Drawn a Igoe Experiments SENN EINUNSNSAISNRUNNINI cyc iA Gene Trees A cre Le EE Erperiment Trees SEPEDA c Classifications _ ANN 242 e Pathways ETS NT Array Layouts SEEME cco Co 0 8 Drawn Genes 40 External Program Trust Bookmarks Embryonic i Scripts B 4 gt 4 Figure 3 12 Compare Genes to Genes Copyright 1998 2001 Silicon Genetics 3 24 Viewing Data in GeneSpring Compare Genes to Genes In the Compare Genes to Genes view GeneSpring employs a Pearson correlation to measure the pair wise similarities see Pearson Correlation on page L 2 Note that if you place the same list on both axes you will see a line of perfect correlation values descending diagonally across the grid To view Compare Genes to Genes 1 Click the first gene list that you wish to compare in the navigator Please do this before you switch the view type as large gene lists will take a very long time to compare 2 Selectthe View gt Compare Genes to Genes option The def
407. the organism name or the brand name of your array and click Next c Continue providing the information requested on each screen and click Next until you have completed the wizard For details see Genome Wizard on page C 1 If you choose to skip this step the Autoloader used in Step 2 will load gene information directly from your data files However if you want to retrieve annotations for your genome using the GeneSpider Step 4 you will have to enter the GenBank accession number of each gene in col umn 10 of the master gene table that was created by the Autoloader Silicon Genetics can provide annotated genomes for many of the most commonly used arrays Please call 1 866 SIG SOFT or email support sigenetics com for details e Step 2 Load an Experiment a SelectFile gt Autoload Experiment b Choose a file c Either GeneSpring will recognize the format of your data file and ask you to name your genome or you will have to set up columns using the column editor To Set Up Columns 1 Click each of the cells in Function row and choose a data type from the pull down menu 2 Click the Load Now button a GeneSpring will ask you if you would like to load more files for this experiment If you have additional files click the appropriate box otherwise click No Load Only This File b Enter an experiment name into the Choose Experiment Name window and click Save Copyright 1998 2001 Silicon Genetics 1 9 Introduction Gene
408. them in a spreadsheet formatted as two columns with one row per gene simply save this file as a tab delineated text file SGD A third Master Gene Table format is SGD This is the format used for the list of genes in the Saccharomyces Genome Database SGD and is generally only relevant for yeast As yeast comes pre loaded in GeneSpring details about this format are unnecessary Mapped The fourth and most sophisticated Master Gene Table format is Mapped Again this format has one line per gene with several fields separated by tabs The first field systematic name must be present all other fields are optional The fields are described below When creating your Master Gene Table these fields should be entered in the order listed here 1 Systematic Name The normal way of referring to this gene This name must be unique The name entered in this field can be utilized by the Find Gene command to find this particular gene within GeneSpring It is recommend that the name used as the gene s systematic name be the name which labels that gene s raw control strength val ues in your experiment data files Any of this information can be accessed when you use the Find Gene command 2 Common Name An alternative way of referring to this gene The name entered in this field can be utilized by the Find Gene command to find this particular gene within GeneSpring Genes are not required to have a common name and common names do not ha
409. ther e g per sample normaliza tions this should probably be a small number like 0 01 Obviously this normalization needs more than one sample to make sense It can be considered a synthetic control Mathematical Illustration of the Normalizing Each Gene to Itself Method Data normalized by Normalize Each Sample To Itself After Normalizing Each Sample to Itself Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1 1 3 CLN2 1 1 1 CDC28 0 1 0 1 0 1 HSL1 1 1 1 YGP1 10 10 10 Appendix G 8 Copyright 1998 2001 Silicon Genetics Normalizing Options The results of normalizing each gene to itself Normalizing Each Gene to Itself After Normalizing Each Gene to Itself Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1 1 3 CLN2 1 1 1 CDC28 1 1 1 HSL1 1 1 1 YGP1 1 1 1 Data normalized by Normalize to Positive Controls After Normalizing to Positive Controls Gene Name Sample 1 Sample 2 Sample 3 CLN 1 0 5 0 5 1 5 CLN2 0 5 0 5 0 5 CDC28 0 05 0 05 0 05 HSL1 0 5 0 5 0 5 YGP1 5 5 5 The results of normalizing each gene to itself After Normalizing Each Gene to Itself Gene Name Sample 1 Sample 2 Sample 3 CLN 1 1 1 3 CLN2 1 1 1 CDC28 1 1 1 HSL1 1 1 1 YGP1 1 1 1 See Experiment Normalizations on page 2 21 for how to implement this normalization option from within GeneSpring
410. this process Remembered Formats While you cannot edit remembered formats you can share them If you need to change a remem bered format you will have to build a new one To share remembered format files use your favorite browser or file management program to copy the file from YourLocalDrive Program Files SiliconGenetics GeneSpring data Experiment Formats name expformat You can then paste the file into a shared drive Copyright 1998 2001 Silicon Genetics 2 5 Creating DataObjects in GeneSpring Merging Splitting and Duplicating Experiments Merging Splitting and Duplicating Experiments The Merge Split Experiments function allows you to merge or split experiments or groups of experiments in their entirety or by condition Note that only conditions from your default interpre tation are available for merging splitting GeneSpring also allows you to duplicate experiments Once you merge an experiment you can treat it like any other experiment with a few notable exceptions If you have multiple spots for one gene on a single chip GeneSpring will only retain the median of those values in the merged experiment This means that you will not have access to error bars Also GeneSpring will only be able to access data from the following columns gene name signal signal background signal precision control channel control channel background description GenBank ID flags and region To Merge or Split an Experiment 1 SelectExperim
411. tion This makes the gene lists in the selected folder the classifications for the genes being displayed The result should look like several lines of genes across the genome browser 4 Zoom in If your computer screen is small you may not be able to see the classification names and you will need to enlarge GeneSpring s main screen Make the screen bigger by clicking the border and dragging the borders outwards In particular make the screen taller You can also click and drag at the edges of the genome browser making the navigator and the colorbar smaller Copyright 1998 2001 Silicon Genetics 3 27 Viewing Data in GeneSpring Functional Classification gt GeneSpring 4 1 Yeast Genes all genes File Edit View Experiments Colorbar Tools EH Gene Lists J Gene Ontology EHS biological process lists cell communication lists cell growth and maintenance developmental processes i physiological process cell communication cell growth and maintenance 8 developmental processes 48 physiological processe cellular component lists molecular function lists _ PIR keywords all genes all genomic elements like YMR199W CLN1 0 95 Experiments Gene Trees Experiment Trees Annotations Window Help cell communication cell growth and maintenance evelopmental processes physiological processes unclassified 1 0000 DORMA OLOA OUG 0S O O UOA OAO OM
412. tion from 1 option is selected error is approximated by using the median deviation from 1 0 The goal in this step is to remove outliers when replicates are being used and to disregard genes whose high or low expression level is the result of biological activity In the absence of rep licates the working assumption is that the vast majority of the genes do not change over the condi tions in the experiment and thus deviation from one represents error in a gene whose expression level changes little over the course of the experiment Then an iteratively reweighted linear regression of variation or squared deviation versus squared control strength is fitted to estimate the parameters Estimation of the 2 level variance components model is done by the method of moments In order to eliminate negative estimates of variance components within sample variation is taken as a lower bound on total between sample variation Different sources of information in the analysis are weighted by their appropriate statistical degrees of freedom Precision estimates based on rep licate genes or samples are assigned degrees of freedom equal to the number of replicates minus one User supplied precision values if available are assigned 1 degree of freedom Cross gene error models if used are assigned an equal number of degrees of freedom as the direct variability estimates for that gene Between sample analyses are done according to the interpretation mode ratio log
413. tion with your cursor All the genes associ ated with that node will change to your selected color A single green line ending in a gene is a branch of the gene tree Each bar crossing a set of branches forms a node of the intersecting branches The distance from gene X to the node con necting it to gene Y indicates how closely the genes X and Y are correlated The shorter the dis tance the higher the correlation is You can also create a new tree from a node of a larger tree Select a node as described above then right click in the genome browser and selectMake Subtree from the pop up menu Viewing Nodes After clustering the genes according to their expression patterns GeneSpring checks all known lists against all subtrees of the new gene tree to assign names to the tree nodes where possible These labels are taken from the gene lists in the standard lists e Place your cursor as close as possible to a label or intersection to view the text When the cursor pauses over an intersection a label will appear It will disappear when the cursor is moved All of the branches intersecting to form a node constitute the subtree defined by that node A label such as ribosome 15 1 means the subtree from that node has a lot in common with the genes in the ribosome list The numbers in square brackets are a measure of sta tistical significance The higher the value the more significant the comparison is The comparisons between the lis
414. tions of multiple experiments are done through a weighted correlation in which you spec ify the weight of each experiment You may make one experiment or experiment set more impor tant than another If all of the experiments or experiment sets are given the same weight they will be averaged equally The name of the experiment is noted directly after its relative weight For example you could give SampleExperiment a weight of 2 and Experiment2 a weight of 1 5 2 Copyright 1998 2001 Silicon Genetics Clustering and Characterizing Data in GeneSpring Trees Therefore in this example the correlations found in the SampleExperiment1 will be twice as influential in creating the tree as the correlations between the genes in the Experiment study The equation used to determine the overall correlation is X Aa Bb Cc a b c e A is the correlation coefficient between the gene in question in experiment 1 and the gene named in the Experiments to Use box also from experiment 1 e ais the weight specified for experiment 1 e Bis the correlation coefficient of the gene in question in experiment 2 to the gene named in the title bar also from experiment 2 e bis the weight associated with experiment 2 e Cis the correlation coefficient of the gene in question in experiment 3 to the gene named in the title bar also from experiment 3 e cis the weight associated with experiment 3 and so on Experiments 1 2 3 and so forth
415. tistical Group Comparison Restriction can be applied to entire experiments e Expression Restriction can be applied to single conditions or samples e Condition to Condition Experiment Restriction can be applied to single conditions or samples e Data File Restriction can be used for either entire experiments or single conditions or samples Details about the types of restrictions you can make are described below A sixth option Inspect brings up the appropriate Inspector information window 4 You can repeat steps 2 and 3 applying several restrictions at one time To remove a restriction click the text of the restriction in the Restrictions box and click the Remove button 5 Click OK to make the list Alternatively click the Make List button to name and save the new list without closing the Filter Genes window if you wish to continue applying filters 6 Choose a name and destination folder for your new list and click Save 4 2 Copyright 1998 2001 Silicon Genetics Analyzing Data in GeneSpring Filter Genes Analysis Tools Restrictions Over an Entire Experiment or Interpretation Restricting by Expression Percentage This restriction finds genes with certain values present in some of the conditions or samples in an experiment or interpretation You can set what proportion of conditions must meet a certain threshold For example if you want to eliminate genes that do not meet a specified control value at least once in the experiment
416. tly across multiple samples or those with expression levels that are too close to the background Filtering genes also allows you to search for genes that are differentially expressed over two or more conditions Filtering Genes 3 Filter Genes E lol x EH Gene Lists Gene List Gene Ontology Choose from Genes all genes seer PIR keywords 3 868 of 6 308 genes pass restrictions all genes 8 all genomic elements Ls like YMR199WV CLN1 0 ONCO_PREDICT EHS Experiments GHI Random Data time series Gti Yeast cell cycle time serie Re stricvons eeseusesceecseececossvenseoss A A R A A a a OTAL NUMBER OF GENES IN EXPERIMENT lt lt Remove OTAL NUMBER OF GENES PASSING THE CURRENT RESTRICTION OK Cancel Help Figure 4 1 The Filter Genes window 1 Select Tools gt Filter Genes If you want to change the gene list select a different gene list from the navigator panel of the Filter Genes window 2 Right click an experiment sample or condition in the navigator Copyright 1998 2001 Silicon Genetics 4 1 Analyzing Data in GeneSpring Filter Genes Analysis Tools 3 Select one of the five restriction options available from the pop up menu You will be prompted for information about the type of restriction you want to make There are five types of restrictions available e Expression Percentage Restriction can be applied to entire experiments e Sta
417. to allow you to name the output file To use the Load feature click on the appropriate Load command a file selector dialog will appear to allow you to choose the file to load and when the data is loaded then a new data object dialog will appear to allow you to name the data object and put it ina GeneSpring folder if you desire These programs are all contained in one jar file called FileAccess jar that needs to be placed in the Programs subfolder of the GeneSpring Data folder on your hard disk You can get the latest version of this file from http www sigenetics com cgi SiG cgi Products GeneSpring extProgs smf Download the jar create a Programs folder in your GeneSpring Data folder if needed put the jar file in it and restart GeneSpring You should now have several new items under the External Pro grams menu in the GeneSpring navigator If your External Programs menu is getting cluttered you can create a folder within the Programs folder e g File Access and put the FileAccess jar file inside that folder the File Access items will then appear in the correspondingly named sub folder of the External Programs folder Copyright 1998 2001 Silicon Genetics 4 44 Clustering and Characterizing Data in GeneSpring Trees Chapter 5 Clustering and Characterizing Data in GeneSpring Trees The classification of organisms into phylogenetic trees is a central concept to biology Organisms sharing properties tend to be clustered together
418. to print or copy Double clicking a list brings up a Gene List Inspector for that list Figure 3 22 shows the Gene List Inspector window for the like YMR199W CLN1 0 95 list gt Gene List Inspector like Y MR199 W CLN1 0 95 117 genes E Oo xj History 5 o 4Normalized name like YMR199 V CLN1 0 95 Intensity ratio Author YourName Organization Xtal Application GeneSpring 4 1 Created Wed Sep 12 02 42 10 PDT 2001 Note Genes with correlation of at least 0 95 to YMR199 CLN1 in expe Genome Yeast Contents Gene List Number 117 Numbers Correlation Coefficient to YMR199V CLN1 Selected From all genes Experiment Yeast cell cycle time series no 90 min Default Interpretation Restriction Correlation at least 0 95 against YMR199V V CLN1 Similar Lists 0 0 like YMR1 99W CLN1 0 95 4 1080373E 8 cell cycl Ecit time minutes 50 80 110 460 Use as Standard List Gene Correlation Coefficient to Description a 1 YMR199vV G sub 1 cyclin YKLO42 0 9843 tofth indl le bod component of the spindle pole body E YDR507C 0 9843 putative serineVthreanine kinase Print List Similar lists P value List Name Copy to clipboard 0 0 like YMR199VWV CLN1 0 95 41080373E 8 cell cycle Rename List 1 04694344E 7 DNA replication and chromosome cycle 8 314177E 6 DNA metabolism Find Regulatory Sequences 4 885825E 4 DNA repair
419. to see if anything new has been added to the public databases from which your information came Appendix P 4 Copyright 1998 2001 Silicon Genetics Common Commands Common Commands in the Genome Browser Common Commands in the Genome Browser Right clicking in the genome browser will bring up a list of commands that can be performed from that window Some of these commands are also available when right clicking in the main screen of the Gene Inspector Mac Users should use Control Click to activate pop up menus e Zoom Out Clicking the Zoom Out button or menu option under View will zoom out by a factor of two as will Ctrl You can also use Edit gt Undo to go back to the previous level of magnification e Zoom Fully Out This command returns the screen to its original magnification state a mag nification value of 1 Select View gt Zoom Out Zoom Fully Out is also in the menu resulting from a right click while the cursor is in the genome browser The Home key will also zoom the genome browser fully out e Make List from Selected Genes This command allows you to make a new list from the genes highlighted in the genome browser To use this command right click in the browser dis play window and a menu will appear Go to the Make List from Selected Genes command and click it A New Gene List window will appear For more information about this window see New Gene List window on page 4 11 If there are no genes selected this comm
420. to using GeneSpring and loading data designed to get you up and running in the shortest possible time Figure 1 2 depicts the steps in a typical analysis session using GeneSpring Note that this diagram represents what might occur in a typical data analysis session and does not include all of the types of analy ses found in GeneSpring export data and or images for use in publication or target validation publish to retrieve from GeNet Figure 1 2 Typical GeneSpring workflow In loading your data you will come across terms and concepts such as genome parameter param eter values replicate interpreted data etc Below are explanations of how these terms are used in GeneSpring Copyright 1998 2001 Silicon Genetics 1 7 Introduction GeneSpring Basics What is meant by a Genome A genome contains information about all the genes in your chip or microarray setup Note that a GeneSpring genome does not correspond exactly to the biological definition of a genome A genome in GeneSpring is composed of discrete genes as opposed to the full nucleotide sequence This means that a GeneSpring genome can contain two genes representing alternatively spliced variants of a single gene whereas a true genome would only include the DNA sequences for one What is meant by a Parameter Parameters are experiment variables such as stage time concentration etc Parameter values are values assigned to experiment parameters For example Embryonic
421. total number of genes and c is the total number of classes This number is useful for comparing the quality of classifications that contain a different number of classes The index of quality G takes into account the number of classes so the quality will not rise limitlessly as the number of classes is increased For example a clustering method that pro duces six classes may explain 60 of the variability and one that produces 10 classes may explain 87 of the variability However when the number of classes is increased to 20 the percent of explained variability may drop suggesting that 10 classes is a more effective classification than 20 Thus the percentage of explained variability is useful in determining the optimum number of groups for a given clustering analysis Copyright 1998 2001 Silicon Genetics 3 47 Viewing Data in GeneSpring The Inspectors Fe lassication Inspector rT Name 5 cluster K Means for Brain demonstration Default Interpretation Authors Bay Emon Research Group Silicon Geneties iti 3XDt Organization Demo User Identifier internalH 3823 Created Thu Mar 22 03 39 11 PST 2001 Notes K Means clustering of the gene list all genomic elements based on weight 1 0 Brain demonstration Default Interpretation Correlation type Distance Converged after 21 iterations crass Genes average Radius 4 set3 169 1 2155496 2 set2 305 1 3617166 3 sett 41 2 817457 4 set4 56 3 9224675
422. ts Analysis scsiccsvcssvcacietavscanecdgjaccesnd4eecebacdesdbengs Wadeenssccasdabncssagedes 5 5 References for Principal Components Analysis cccscceseeeeceeseeesceeeeeeeeeeeaees 5 8 K M ans Clustering 5 acestahsScsyovespeasbineaicadaa R R EE e AE A ATAA AREAREN 5 9 Viewing k means clusters ssssesseesseseessesseeseosseeseesresseesessressteseesesstresseeseeseessee 5 11 oe Git PanIIT IAPS hsr oaaae t A O E E A E TA 5 12 Mewn SOMS ooo a ee a e et a a ates ua R E 5 13 TheClass Predictor eitem ana aa a a a a a oe R E 5 15 Interpreting the Results of a Prediction c ccctccsssessccsecesvescssssednsesadadersesedoos deans 5 16 Chapter 6 Exporting GeneSpring Data eessooesooessoesssesssesssoossoossoossssessoessoossosssssssssee 6 1 Saving Pictures and Printing cis scssctecsacreceuieeeiacta cava uareasaeds aa ieinwnieiniete eee nies 6 2 Exporting Gene Lists out of Gene S pring ssi cis siete noes acssea tated ea ieeteiceeds 6 3 P blshto Ge NCL aay saiar act lt a cy Sevens se cena gs mascot ces aus ySaavae A a aaar i Eh 6 6 Upload to Genet ax cen sansa see E i yaaa anes auctor ater 6 6 Using GENe teenis aa an tate esietent ca lala aia E E a lalate 6 8 Loading Data from GENet size citiscie ss Buin tewestevectatedte ses ae aa ee eae es 6 8 Appendix A Help seistviacessssssscopnsnaevsoncvasdeconssieveducosadaseanscanpuabisadvabecuundeguasseadecencasspesbeaupecsie A 1 Contacting Silicon Genetics Technical Support ssssssss
423. ts and the subtrees are not looking for exact matches but rather statistically significant overlaps which may include subsets and supersets When there is enough space on the screen a label if one exists will be displayed along the top horizontal bar of the subtree Otherwise when there is space a will be displayed An amp symbol after a list name indicates the subtree is statistically similar to more than one list all of whom when there is enough room are displayed as labels along the top of the subtree If you want to take a screen shot that includes the label hover your cursor over the node take the screen shot when the label appears For most Windows applications the cursor will not be visible just the label For more information about screen shots please refer to Saving Pictures and Print ing on page 6 2 Copyright 1998 2001 Silicon Genetics 3 18 Viewing Data in GeneSpring Tree View Viewing Gene Names in Trees You can magnify the tree until the names are visible along the edge of the genes 1 Place your cursor anywhere over the group of genes to view the gene name When the cursor pauses over a gene a label will appear It will disappear when the cursor is moved 2 Click once and that gene will become the selected gene The name of the selected gene will appear in the upper right corner of the genome browser Viewing Colors in Trees The coloring scheme of the current view is shown in the c
424. u are doing this make sure to indicate the column containing the region specification for every sample Experiment RegionColumn number of the column the region specification is found in for the experi ment indicated Experiment1RegionColumn 1 Experiment 2RegionColumn Experiment3RegionColumn 1 N The required layout file for Region Specifications 16 If you have region specifications you must have a layout file See The Layout file on page K 2 for everything this file can or should contain Tell GeneSpring where to find this file Layout complete name of the layout file Layout AffyYeastLayout4 txt Locate the Data Column 17 Which column of your data file contains the raw data reading for Sample 1 Experiment IntensityColumn number of the column containing the raw data for the sample indicated Appendix J 9 Copyright 1998 2001 Silicon Genetics Installing from a Text File Locate the Data Column ExperimentlIntensityColumn 4 Experiment2IntensityColumn 9 Experiment3IntensityColumn 14 Experiment4IntensityColumn 19 Experiment5IntensityColumn 24 Experiment6IntensityColumn 29 Experiment7IntensityColumn 34 If your data is all in the same file you will have to indicate the raw data column for each sample illustrated above This is also true if you have two or more data files with different columns con taining the raw data On the other hand if you have separate data files with the same
425. u export your data GeneSpring reinterprets the data as the ratio Measurements below 01 are exported as 01 Fold Change Fold change mode creates a more balanced visual representation between over and underex pressed genes than Ratio mode and emphasizes the increase and decrease of expression levels For example x1 would refer to normal expression x2 to an expression level twice normal and 2 to an expression level half normal When using the upper or lower bound fields to change the ver tical axis range enter either the ratio values in integers or the fold change value 1 e x4 or 4 Any integers you enter will be converted as in Table 2 1 Yeast cell cycle time series no 90 min time minutes 0 10 20 30 40 50 60 70 80 100 110 120 130 140 150 469 Figure 2 5 New Fold Change Image Note that in Fold change interpretation the lowest measured value is 0 01 Any values below 0 01 will be calculated as 0 01 The minimum display value is 10 Note also that when you export your data GeneSpring reinterprets the data as the ratio Measurements below 01 are exported as 01 Copyright 1998 2001 Silicon Genetics 2 19 Creating DataObjects in GeneSpring Changing the Experiment Interpretation Ratio Numbers Display 5 110 0 110 01 100 this is the lower cutoff 25 4 32 3 5 2 1 5 x1 5 3 x3 5 x3 Table 2 1 Fold Change Parameter Display Modes Continuous Element
426. ue an average a medium control strength value and an unreliable a low control strength value in the three boxes Any gene with a control strength above the value indicated as a high control strength will be colored using the brightest color appropriate any gene with a control strength below the value given for unreliable data will be dull in color The medium signal value gives the value for the mid point of the colorbar and genes with an average control strength are colored halfway between the two color extremes Appendix D 15 Copyright 1998 2001 Silicon Genetics The Experiment Wizard The Experiment Import Wizard 29 For more information on how trust is expressed in the genome browser please see the Changing the Experimental Data Range on page 3 36 Defining default x and y values The middle section of the Wizard panel allows you to inspect the genes expression profiles more closely from the genome browser As GeneSpring does not graph the entire y axis the expression level axis but only the portion most genes profiles fall into you will need to set the defaults for that portion In the lower two boxes indi cate the range of expression levels GeneSpring should graph The values indicated here can be altered within GeneSpring look in View gt Change experiment interpreta tion Here you are simply setting the defaults Defining Negative Values to Zero The bottom section in the Wizard panel asks if you would like to fo
427. ues ReferenceBackColumn 10 Measurement Flags 21 If your data file has a notation flag indicating whether or not the experiment worked for each gene indicate which column contains this information If your data does not include this information skip this question and the associated experiment file entries Experiment OkColumn number of the column saying whether or not the experiment indicated worked for each gene Experiment10kColumn 8 Experiment20kColumn 13 Experiment30kColumn 18 Experiment40kColumn 23 Experiment50kColumn 28 Experiment6O0kColumn 33 Experiment7OkColumn 38 If your data is all in the same file you will have to indicate the experiment worked column for each sample illustrated above This is also true if you have two or more data files with different col umns containing the experiment worked information If on the other hand you have separate data files with the same column containing the experiment worked notation you may use the general object name given below rather than entering the column number of the reference s background values for each file OkColumn number of the column saying whether or not the experiment worked for each gene OkColumn 11 Appendix J 12 Copyright 1998 2001 Silicon Genetics Installing from a Text File Associating a Picture with a Sample 22 If you have a column indicating whether or not your experiment worked what is the designa tion used in this co
428. umber Copyright 1998 2001 Silicon Genetics 4 35 Analyzing Data in GeneSpring Creating Your own Scripts Select Sequence Selects 1st Sequence if Boolean is True and selects 2nd Sequence if Boolean is false 1 Boolean input amp 2 Sequence inputs Output is a Sequence 3 Clustering Build Experiment Tree Makes an Experiment Tree 1 Gene List input amp 1 Experiment interpretation input Knobs for Correlation type Separation ratio amp Minimum distance Output is an Experiment Tree Build Gene Tree Makes a Gene Tree 1 Gene List input amp 1 Experiment interpretation input Knobs for Correlation type Discard bad Separation ratio Minimum distance Do automatic annotation amp Use standard lists Output is a Gene Tree Explained Variation Computes the proportion of variation in an experiment interpreta tion explained by a classification and a gene list 1 Classification input 1 Experiment interpretation input amp 1 Gene List input Output is a number between 0 amp 1 inclusive i e 0 14567 is 14 567 explained variability K means Makes a k means classification 1 Gene List input amp 1 Experiment interpreta tion input Knobs for Number of groups Correlation type Maximum iterations Addi tional tries amp Discard bad Output is a Classification Refine K means Make a k means clustering starting from a classification 1 Classifica tion input 1 Gene List input amp 1 Experiment interpretation input Kno
429. ur default directory in GeNet 1 Classification input No knobs or outputs Send Experiment to GeNet Publish an Experiment interpretation to your default direc tory in GeNet 1 Experiment interpretation input No knobs or outputs Send Experiment Tree to GeNet Publish an Experiment tree to your default directory in GeNet 1 Experiment Tree input No knobs or outputs Send Gene List to GeNet Publish a Gene List to your default directory in GeNet 1 Gene List input No knobs or outputs Send Gene Tree to GeNet Publish a Gene Tree to your default directory in GeNet 1 Gene Tree input No knobs or outputs Specified Directory Send Classification to Directory in GeNet Publish a classification to a chosen direc tory in GeNet 1 Classification input Knob for Directory No outputs Copyright 1998 2001 Silicon Genetics 4 37 Analyzing Data in GeneSpring Creating Your own Scripts e Send Experiment to Directory in GeNet Publish an Experiment interpretation to a chosen directory in GeNet 1 Experiment interpretation input Knob for Directory No outputs e Send Experiment Tree to Directory in GeNet Publish an Experiment tree to a chosen directory in GeNet 1 Experiment Tree input Knob for Directory No outputs e Send Gene List to Directory in GeNet Publish a Gene List to a chosen directory in GeNet 1 Gene List input Knob for Directory No outputs e Send Gene Tree to Directory in GeNet Publis
430. ur gene and links to databases To reach the Gene Inspector window Double click on a gene this may be easier after zooming in Or 1 SelectEdit gt Find Gene 2 Enter the name of your gene 3 Press Ctrl I Copyright 1998 2001 Silicon Genetics 3 37 Viewing Data in GeneSpring The Inspectors gt Gene Inspector YIRO1OW Common name MET3 AS SSS ttest EC number 2 7 7 4 T ftime 0 minutes 0 54940116 time 10 minutes 0 261 76757 pe ee Ub iol 3 time 20 minutes 0 0 48621094 Description ATP sulfurylase 4 time 30 minutes 1 0 15922752 Other Notes 5 time 40 minutes 0 045423114 6 time 50 minutes 0 03996886 inca time 60 minutes 0 16354981 B time 70 minutes 1 0 77882296 3 time 80 minutes 0 3266186 Save Not wm 0 Normalized Yeast cell cycle time series no 90 min colored by Parameters 4 0 Intensity ratio Error Bars within sample std error o 10 20 30 40 50 60 70 8 amp 0 100 110 120 130 140 150 460 Lists Containing YJRO10vy all genes all genomic elements cell growth and maintenance metabolism amino acid and derivative metat amino acid metabolism aspartate family amino acid me methionine metabolism Search SGD Locus Page Search SGD Search MIPS Search GenBank Search Sacch3D methionine metabolism sulfur metabolism Search PubMed sulfur utilization enzyme Search Swiss Prot wm gt Search PIR Minimum
431. ve Classification as radio buttons Select a name for you classification list folder and click Save Viewing SOMs SOM results are best shown using the Split Window feature Each graph contains the genes asso ciated with a SOM node Node numbers are shown in the upper right corner of each plot Copyright 1998 2001 Silicon Genetics 5 13 Clustering and Characterizing Data in GeneSpring Self Organizing Maps gt GeneSpring 4 1 Yeast Genes all genomic elements File Edit View Experiments Colorbar Tools Annotations Window Help 1 1 1 262 genes 5 04Normalized 5 04Normalized 1 3 728 genes Figure 5 6 A 3x2 SOM of the Yeast cell time series no 90 min experiment If you have selected many panels you may want to hide the horizontal and vertical labels for eas ier viewing Right click the genome browser and select an option from the Options submenu You can also increase your viewing space by selecting View gt Visible gt Hide All If you use a SOM to produce a classification you can get details about the classification from the Classification Inspector For information about the Classification Inspector see Classification Inspector on page 3 46 To recreate your SOM graph right click the SOM classification or the folder of gene lists in the navigator and select Split Window gt Both SOM References Kohonen T 1990 The Self Organizing Map Proc IEEE 78 9 1464 1480 Kohonen T 2000
432. ve to be unique although duplicated common names may lead to confusion if the common name is how the gene is referred to in the experiment files This informa tion can be accessed when you use the Find Gene command 3 Map Mapping information for this gene Sequence position for example a first chro mosome gene would be 1 228836 229309 inclusive For an example of the mapped Cytogenetic position such as 16q12 1 4 EC number The EC number for this gene if known Copyright 1998 2001 Silicon Genetics Appendix H 2 Creating Folders for New Genomes Raw Data 5 Description A description of this gene if known This information can be accessed when you use the Find Gene command 6 Product The protein product coded for by this gene if known This information can be accessed when you use the Find Gene command 7 Phenotype A description of the phenotype for this gene if known 8 Function A description of the function of this gene product if known 9 Keywords Keywords associated with this gene if known Separate keywords with semicolons This information can be accessed when you use the Find Gene command 10 GenBank Accession Number The GenBank identifier for this gene if known If the GenBank identifiers for your genes were not used as either their systematic or com mon names then including the GenBank Accession Number in this field allows you to update the information about this particular gene directly from GenBank S
433. w and Compare Genes to Genes view lists made from the Venn diagram are ordered according to the values associated with the lists you used to create the Venn Diagram When more than one of these lists has values genes are ordered according the values of the last list added to the Venn diagram when it was created Copyright 1998 2001 Silicon Genetics 4 20 Analyzing Data in GeneSpring Making Lists from Classifications Making Lists from Classifications You can generate gene lists from any classification For example if you have a 5 cluster k means classification you can view which genes are in each cluster by making a gene list from the k means classification To make a Gene List from a Classification 1 Right click a classification in the Classifications folder in the navigator 2 SelectMake gene lists GeneSpring will create a gene list folder for the classification containing one list for each cluster You will find this folder in the Gene Lists folder in the navigator Find Interesting Genes The Find Interesting Genes command finds genes that have gone through the largest expression changes during the experiment and have high trust values To find Interesting Genes 1 Select Tools gt Find Interesting Genes A dialog box will appear showing one of the most interesting genes in your experiment 2 Click the button in the box The Gene Inspector for that gene will appear See Gene Inspec tor on page 3 37 for info
434. w when different conditions are indicated with discrete symbols To Color by Parameter 1 SelectExperiments gt Change Experiment Interpretation 2 Choose the parameter s you wish to color by and click Color Code for that parameter Click SAVE to create a new interpretation Copyright 1998 2001 Silicon Genetics 3 33 Viewing Data in GeneSpring Changing the Coloring Scheme 3 SelectColorbar gt Color by Parameter GeneSpring 4 1 Rat Genes interesting genes File Edit View Experiments Colorbar Tools Annotations Window Help Gene Lists 5 0 Experiments FE NIH Spinal Cord Study 4 0 Default Interpretation J All Samples 3 0 X Color Code Interpretation 20 To stage Postnatal day 0 stage Z Adult day 0 40 TOF stage Postnatal day 7 O stage Embryonic On stage Embryoni day 7 Animate Magnification 1 wl zoomi out THE CONDITIONS OF PARAMETER VALUES IN THIS INTERPRETATION ALPHABETIC ORDER Figure 3 16 The NIH Spinal Cord Study colored by parameter No Color This option allows you to view genes with no coloration showing all genes in gray To implement this option select Colorbar gt No Color Color by Classification This coloring scheme allows you to color code the genes by some previously defined knowledge about them You can use a folder of lists to color by classification or a classification method such as k mean
435. ximum of one picture with each condition Even with only a few pic tures GeneSpring will display the picture closest to the condition you are viewing These pic tures should be either gif or jpeg files e Pictures of the Microarray plates At most there can be one array picture associated with each sample These pictures should be either gif or jpeg files Appendix D 1 Copyright 1998 2001 Silicon Genetics The Experiment Wizard Files You will Need to Use the Experiment Wizard e The Positive and Negative Control Files A positive control file and a negative control file are formatted in exactly the same way their contents are different Each file lists the control genes names one name per line Control Gene Name 1 Control Gene Name 2 Control Gene Name 3 Control Gene Name 4 Control Gene Name 5 Control Gene Name 6 This list of gene names is all either file should contain There should not be any headlines or any thing else in the file only the gene names Briefly you have negative controls in your experiment when there is DNA from a different genome than the one you are investigating on the array You are using positive controls when there is DNA from a different genome than the one you are investigating on your array and you add a known quantity of that different DNA to your sample For a description of the possible nor malizations to be done with these controls see Normalizing Options on page G 1
436. xperiments Colorbar Tools Annotations Window Help Gene Lists Selected YGLOGOW LIF 1 Experiments Gene Trees Experiment Trees Classifications HJ Pathways Array Layouts A i amp L Drawn Genes HL External Prograrr Bookmarks Scripts so on nO nox mMm Figure 3 10 The Array view In Figure 3 10 each solid circle represents an oligonucleotide on the array If you zoom in the gene names will become visible To view an Array Layout 1 Select the View gt Array Layout option 2 Select an array from the navigator Copyright 1998 2001 Silicon Genetics 3 22 Viewing Data in GeneSpring Pathway View Pathway View The Pathway view lets you display and place genes on an imported gif or jpeg image Fie Edit Yiew Experiments Colorbar Tools Annotations Window Help E Gene Lists Gt Gene Ontolo HHJ PIR keyworet 3 all genes 8 all genomic 3 ACGCGT in L8 like YMR199 E3 Experiments 1O x EH Gene Trees E Experiment Tret E Classifications EH Pathways E Cell growth EY mitosis FH Array Layouts EH Drawn Genes E External Progral E Anokmarks so on nO tGB xm Figure 3 11 The Pathway view To view a Pathway 1 Select a pathway from the Pathways folder in the navigator You will need to have already created a Pathway See Pathways on page 4 23 2 Select a gene list If a pathway contains a gen
437. y Each Sample to Itself D 14 Normalizations by Negative Controls D 13 Normalizations by Positive Controls D 13 Normalizations Each Sample to a Hard Number D 14 Number of Arrays D 4 Number of Parameters D 5 Parameter Characteristics D 5 Parameter Values D 5 Properties of Experiment D 4 Region Normalization D 8 RT PCR Experiments D 12 Sample Photos D 11 Welcome D 3 y axis J 19 Z zoom out P 5 Index 6

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