AffyMiner - a Tool for Significant Gene Mining in Affymetrix
Contents
1. 14 To run ChipInfo the gene information file downloaded from the Affymetrix website is required ES GeneFinder Gene Annotation Result probe_set 1622893_at 1622920_at 1622960_at 1623028_at 1623066_at 1623069_s_at 1623065_s_at 1623083_at 162311 7_at 1623126_at 1623126_at a7 Proceedings of the 40th Hawaii International Conference on System Sciences 2007 Average_Signal_Lo 0 608688888885 0 584444444444 0 846666666666 0 85 0 636666666666 0 636666666666 1 27 0 67 0 814444444444 0 814444444444 Target Description CG16844 RA FE CG7875 RA FE CG14902 RA FE Gene Title Immune induced transient receptor death executione CG14872 RA FE CG10369 RA FE Inwardly rectifying CG17544 RA FE CG17544 RA FE CG1698 RA FE CG31781 RB FE CG13912 RA FE CG13912 RA FE Gene Ontology Protein Families 5200 structural 8086 light acti 30693 caspas 5242 inward re 16402 7 pristano 16402 77 pristano ec ACDM_DR lec 7 ACDM_DR 5328 neurotra se i Annotation en Figure 7 Annotation table resulting from the Annotation program in AffyMiner The GO tree generated from AffyMiner is shown in Figure 8 Each node is labeled with the corresponding GO term GO ID and the number of genes associated F2. calculating gene expression from short oligonucleotide array data will give different results BMC Bioinformatics vol 7 pp 137 2006 Z Wang X Zhang P Shen B W Loggie Y Chang and T F Deuel A variant of estrogen receptor alpha hER alpha 36 transduction of estrogen and antiestrogen dependent membrane initiated mitogenic signaling Proc Natl Acad Sci U S A vol 103 pp 9063 8 2006 R Breitling P Armengaud A Amtmann and P Herzyk Rank products a simple yet powerful new method to detect differentially regulated genes in replicated microarray experiments FEBS Lett vol 573 pp 83 92 2004
3. changed dynamically HE GeneFinder Parameter Settings Select Columns Gene Annotation Result Please enter number of treatment and control replicates Number of treatment replicates Number of control replicates Robust Change increase Decrease Please select the criteria for filtering significant genes Signal Detection Number of P in treatment replicates Signal Change Number of in pairwise comparision Signal Ratio Average Signal Log Ratio or Fold Change 0 0 cece ceca ee 105 T A ee eee net eon tae Cite esse Rtn rect eae ROOT err 0 05 Statistical Test Figure 4 Parameter setting window The next step is to upload input file and select columns corresponding to specific samples i e treatment and control and data metrics Figure 5 The input file is a text file exported from Affymetrix GCOS containing results of single array analyses and pairwise array comparison analyses If Significant Test checkbox was checked in the parameter setting window Figure 4 the results of statistical tests need to be added to the text file with two columns corresponding to the p value and change direction respectively The change direction is specified as up for up regulated genes and down for down regulated genes Clicking Back button returns to the first window if parameter settings need to be changed Clicking Search button starts analysis process Figure 6 shows significant genes found b
4. Signal Log Rate O7 gt T1 we C3 Signal Log Ratio NS gt 12 we CO Sanal Log Rate O21 gt T2 wi C2 Signal Log Patio 4 Slalebcal eel Pvabve 1 cokan needed ORI gt Tratmert ve Contind P Vahar a a Figure 5 Input setting window ES GeneFinder Parameter Settings Select Columns Gene Annotation Result probe_set Average Signal T1_Signal 1T1_Detection T2_Signal T2_Detection T3_Signe 1627613_at 0 97 393 1 P 345 375 1641270_at 0 6044444444 1959 1 965 995 1634439_at 0 5144444444 261 3 313 315 1623398_at 1 0033333333 326 3 341 350 1631400_at 0 9622222222 448 7 411 1624256_at 0 6688889888 331 7 265 nN C 11111111 ACF C aca Annotation GO Tree ACIVATAR a mt Figure 6 Differentially expressed genes found by the Significant Genes program in AffyMiner The table resulting from the Annotation step is shown in Figure 7 where Average Signal Log Ratio Target Description Gene Title Gene Ontology and Protein family were selected This table can be included in publications with no need for further editing 2 3 2 GOTree GOTree takes as input two files The first file called GOPath consists of the information about the hierarchical structure of GO terms whereas the second file contains the list of significant genes and their GO term associations The GOPath file was generated from the Chip nfo program which can be downloaded from the web http www biostat harvard edu complab chipinfo
5. Xia G Lu A Firsov G Sarath H Moriyama J G Dubrovsky and Z Avramova The Arabidopsis homolog of trithorax ATX1 binds phosphatidylinositol 5 phosphate and the two regulate a common set of target genes Proc Natl Acad Sci US A vol 103 pp 6049 54 2006 R Alvarez Venegas Y Xia G Lu and Z Avramova Phosphoinositide 5 Phosphate and Phosphoinositide 4 Phosphate Trigger Distinct Specific Responses of Arabidopsis Genes Genome Wide Expression Analyses Plant Signaling amp Behavior vol 1 pp 140 151 2006 P C Larosa J Miner Y Xia Y Zhou S Kachman and M E Fromm Trans 10 Cis 12 Conjugated Linoleic Acid Causes Inflammation And Delipidation Of White 18 19 20 21 22 Proceedings of the 40th Hawaii International Conference on System Sciences 2007 Adipose Tissue In Mice A Microarray And Histological Analysis Physiol Genomics 2006 G K Smyth Michaud J and Scott H The use of within array replicate spots for assessing differential expression in microarray experiments Bioinformatics Bioinformatics vol 21 pp 2067 2075 2005 G K Smyth Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments Statistical Applications in Genetics and Molecular Biology vol 3 pp Article 3 2004 F F Millenaar J Okyere S T May M van Zanten L A Voesenek and A J Peeters How to decide Different methods of
6. aspect AffyMiner takes full advantage of the range of the different data metrics available from MAS 5 0 AffyMiner provides the flexibility to choose different data metrics Signal Detection Signal Change Signal Log Ratio and Statistic Test and to set threshold values for analyzing differentially expressed genes This flexibility is very important since no one single method outperforms others for microarray data analysis 20 22 This is apparent from the differences in the gene lists found by the AffyMiner and RMA based methods in the case study Incorporating qualitative data metrics such as Detection and Signal Change would increase the selectivity of detecting differentially expressed genes 20 Proceedings of the 40th Hawaii International Conference on System Sciences 2007 GenePicker 10 has certain functions similar to those in AffyMiner GenePicker was developed for the analysis of replicates of Affymetrix gene expression microarrays The GenePicker analysis is done through defining analysis schemes data normalization t test ANOVA and Change fold Chang analysis and the use of Change Call Fold Change and Signal mean ratios GenePicker provides a comparison of noise and signal analysis scheme for determining a signal to noise in a given experiment which is not available in GeneFinder However GeneFinder uses one more data matrix i e Detection As mentioned earlier GeneFinder also has the function of incorporating g
7. tl0c12 CLA 17 The Affymetrix Mouse Genome 430 2 0 microarrays were used to detect the expression changes of about 34 000 transcripts Mice were sampled 1 2 3 4 7 10 or 17 days after being fed control or 0 5 t10c12 CLA diets generating 7 time points in total At each time point the RP WAT tissues of ten control and ten t10c12 CLA fed mice were harvested in groups of five mice each to provide two control and two treatment samples for microarray analysis Proceedings of the 40th Hawaii International Conference on System Sciences 2007 To detect differentially expressed genes the transformed RMA expression values were analyzed using an empirical Bayes Linear model 18 19 A total of 5408 genes were found significant on Day 1 by the RMA approach We used the same dataset and ran AffyMiner with the following parameter settings for increase 2 Present calls in the treatment samples 3 Increase calls for signal change average signal log ratio being 0 5 for decrease 2 Present calls in the control samples 3 Decrease calls for signal change average signal log ratio being 0 5 AffyMiner found 4089 differentially expressed genes The number of overlap genes found by AffyMiner and RMA is 2946 Table 1 The discrepancy of the gene lists is contributed mainly to the differences of algorithms underlying AffyMiner and RMA This observation is consistent with a recent comparison of six different algorithms where only 27 to 36 overlap we
8. to the parent MCF7 cells in the absence or presence of estrogen Affymetrix Human GeneChip Genome U133 Plus 2 0 Array was used which contains over 47 000 transcripts Experimental settings follow standard procedures as described previously 17 AffyMiner was used to identify significant genes with different expression patterns in these cells in the absence or presence of estrogen We used the same parameter settings as in 3 1 A total of 2162 and 1507 genes were found differentially expressed in 12 hour estrogen treatment for the parental MCF7 cells and hER a36 expressing MCF7 cells respectively We are currently performing in depth pathway and ontology analysis and the results will be presented elsewhere 4 Discussion 4 1 Software comparison Microarray technology has revolutionized the analysis of gene expression The challenge associated with this high throughput technology is the statistical analysis and biological interpretation of microarray data AffyMiner was developed to address these issues through finding genes with significant changes in gene expression and linking these genes with the annotation and Gene Ontology information Functionally AffyMiner has overlap with other existing programs but has the distinguishing features discussed below Affymetrix Data Mining Tool DMT can filter genes of interest based on the thresholds of certain quantitative and qualitative parameters but not as powerful as AffyMiner in this
9. Proceedings of the 40th Hawaii International Conference on System Sciences 2007 AffyMiner a Tool for Significant Gene Mining in Affymetrix GeneChip Microarray Data Guoqing Lu The Nguyen Yuannan Xia Zhaoyi Wang Mike Fromm Department of Biology University of Nebraska at Omaha Omaha NE 68182 Center for Biotechnology University of Nebraska Lincoln Lincoln NE 68588 Cancer Center Creighton University 2500 California Plaza Omaha NE 68178 olu3 mail unomaha edu for GL Abstract Microarray technology has revolutionized molecular biology The challenge associated with this high throughput technology is how to analyze and make biological sense of a large amount of microarray data We introduce AffyMiner a tool developed for detecting differentially expressed genes from Affymetrix GeneChip microarray data and connecting gene annotation and gene ontology information with the genes detected AffyMiner consists of three functional modules GeneFinder for finding significant genes in a treatment versus control experiment GOTree for mapping genes of interest onto the Gene Ontology GO space and interfaces for running Cluster a program for clustering analysis and GenMAPP a program for pathway analysis AffyMiner effectively deals with multiple replicates in the experiment provides users the flexibility of choosing different data metrics for finding differentially expressed genes and is capable of incorporati
10. ciations continue to grow in complexity and detail as sequence databases and experimental knowledge grow 13 GO provides a useful tool to look for common features shared within a list of genes The high level description of the algorithm in building the GO tree is as follows 1 read the output file generated by GeneFinder 2 write in an array the GO IDs and their corresponding Affymetix probe set IDs 3 find the GO Path IDs for each GO ID in the array and add the GO Path IDs to each element in the array 4 sort by the GO Path IDs and compute the sum of the probe sets associated with each node 5 build the entire tree based on the GO Path IDs and write in each node the GO term GO ID and the number of probe sets GeneFinder finding up regulated genes Input infile a text file exported trom Affymetrix GCOS Output a list of up regulated genes of Present calls in multiple array comparison m of Increase calls in multiple array comparison n average Signal Log Ration p p value in the statistical test J t 0S Signal Log Ratio open infile while inline lendat iledintiles array lt inline while array Detection Present Sin treatment samples j if je j 0 while array Change l signal change j if jerry j 0 for i 0 to iesizefarray r rt array Signal log ratio j iti gt n amp amp arrayi F walue p amp amp array lOirection up P array probe_set print ar
11. ed genes AffyMuiner limits the maximum number of replicates to five This is a reasonable assumption because the reproducibility of Affymetrix GeneChip array data is high and most publications use two to three replicates in their experiments The data metrics consist of Signal Detection Signal Change Signal Log Ratio and Statistical Test The user can choose the data matrices and threshold values for each analysis As shown in Figure 4 three treatment replicates and three control replicates were used for example analysis The radio button Increase was checked which means the task was to find significantly up regulated genes In the frame Please select the criteria for filtering significant genes checkboxes are used to select which criteria are applied when filtering the genes The signal detection level was set 3 meaning the Present calls in the signal detention value are required to be present in all the 3 treatment replicates The number of signal Change calls was set 8 which means that at least 8 Increases are required in the 9 Proceedings of the 40th Hawaii International Conference on System Sciences 2007 Change calls for any given probe sets considered to be significant The threshold for average signal log ratio was set 0 5 which requires about a 1 4 fold increase of the signals in the treatment samples compared with the control samples The p value for the statistical significance was set 0 05 The above settings can be
12. ene annotation information with expression data which is not available in GenePicker The Affymetrix NetAffx Gene Ontology Mining Tool can create a graph of GO terms associated with the input probe sets However the graph is very difficult to read as compared with the one generated by AffyMiner Figure 7 AffyMuiner has the flexibility of displaying the GO tree at different levels and the probe sets associated with the GO terms can be viewed easily Another GO tool called GoSurfer was developed for the GO analysis of Affymetrix GeneChip data 7 13 14 GoSurfer associates user input gene lists with GO terms and visualizes such GO terms as a hierarchical tree GoSurfer compares two lists of genes in order to find which GO terms are enriched in one list of genes but relatively depleted in another GoSurfer can not map genes from a single list onto the GO descriptions In this regard GOTree and GoSurfer complement each other in the analysis of Gene Ontology 4 2 Limitations AffyMiner is a Windows application It runs only on computers using Microsoft Windows 2000 or above In addition AffyMiner relies on the Affymetrix MAS 5 0 algorithm for the low level analysis in the single array analysis and pairwise comparisons and NetAffx for gene annotation information We recommend the user check the NetAffx website frequently and use the latest annotation file for analysis 5 Conclusions As a whole AffyMiner fills an important gap in
13. ent t test or the Mann Whitney test can be used to test the hypothesis whether the signal intensity values for each probe set are significantly different in the treatment group compared with the control group Such statistical tests are not ideal for finding significant genes because only a few replicate samples lt 4 are usually used in the microarray experiments Determining the most appropriate statistical method for detecting differentially expressed genes in GeneChip replicate data remains a challenging issue Several methods have been developed to improve the sensitivity and selectivity for detecting significant genes in GeneChip microarray experiments The widely used algorithms include the robust multiarray average RMA 6 the model based expression index intensity MBEI implemented in dCHIP software 7 and the positional dependent nearest neighbor model PDNN 8 These algorithms effectively deal with the probe effect that 1s some probes in a probe set tend to give higher values than others 2 through re computing of the signal intensity 1530 1605 07 20 00 2007 IEEE Proceedings of the 40th Hawaii International Conference on System Sciences 2007 for each probe set using the processed image data exported from Affymetrix Microarray Suite or GeneChip Operating Software GCOS These methods rely solely on quantitative data 1 e signal intensity values for comparison analysis Qualitative data such as signa
14. finding differentially expressed genes from Affymetrix GeneChip microarray data AffyMuiner effectively deals with multiple replicates in the experiment provides users flexibility choosing different data metrics for detecting significant genes and is capable of incorporating various gene annotations AffyMuiner has been used for analyzing the GeneChip data for several publications which has facilitated comparing data from multiple arrays and interpreting the possible biological implications associated with significant changes in a gene s expression 6 Acknowledgements This publication was made possible by NSF Grant Number EPS 0346476 from the NSF EPSCoR program and by NIH Grant Number P20 RR16469 from the INBRE Program of the National Center for Research Resources GL acknowledges the Pre tenure Award from University of Nebraska at Omaha The authors are grateful to a number of users for providing feedbacks on AffyMiner Proceedings of the 40th Hawaii International Conference on System Sciences 2007 7 References 1 10 D J Lockhart H Dong M C Byrne M T Follettie M V Gallo M S Chee M Mittmann C Wang M Kobayashi H Horton and E L Brown Expression monitoring by hybridization to high density oligonucleotide arrays Nat Biotechnol vol 14 pp 1675 80 1996 J D Clarke and T Zhu Microarray analysis of the transcriptome as a stepping stone towards understanding biological systems pract
15. g information from Gene Ontology and metabolic pathways e Easy to use graphical interfaces 2 1 2 Architecture Based upon the user requirements and our experience in using commercial and open source microarray analysis programs such as GeneSpring http www agilent com chem genespring and Bioconductor 11 we designed AffyMuiner to include three functional modules GeneFinder GOTree and interfaces to third part programs Figure 1 These modules can analyze GeneChip data separately or consecutively For example the gene list generated by GeneFinder can be used by GOTree or Cluster for further analysis Two popular open source software programs Cluster and GenMAPP were chosen for clustering and pathway analyses respectively Interfaces Ses Figure 1 The Architecture of AffyMiner 2 2 Algorithms 2 2 1 GeneFinder The algorithm implemented in GeneFinder uses both qualitative and quantitative measures of transcript performance including Detection Change Signal Log ratio and the statistical results To determine significantly up regulated genes in an experiment with multiple replicates of treatment and control samples the following steps are used 1 eliminate the probe sets with signal Detection calls of Absent in the treatment samples 2 select the probe sets with signal Change calls of Increase 3 eliminate the probe sets with Signal Log Ratios below a threshold defined by the user a
16. ical considerations and perspectives Plant J vol 45 pp 630 50 2006 E Hubbell W M Liu and R Mei Robust estimators for expression analysis Bioinformatics vol 18 pp 1585 92 2002 W M Liu R Mei X Di T B Ryder E Hubbell S Dee T A Webster C A Harrington M H Ho J Baid and S P Smeekens Analysis of high density expression microarrays with signed rank call algorithms Bioinformatics vol 18 pp 1593 9 2002 Affymetrix GeneChip expression analysis data analysis fundamentals 2006 R A Irizarry B M Bolstad F Collin L M Cope B Hobbs and T P Speed Summaries of Affymetrix GeneChip probe level data Nucleic Acids Res vol 31 pp e15 2003 C Li and W H Wong Model based analysis of oligonucleotide arrays expression index computation and outlier detection Proc Natl Acad Sci U S A vol 98 pp 31 6 2001 L Zhang M F Miles and K D Aldape A model of molecular interactions on short oligonucleotide microarrays Nat Biotechnol vol 21 pp 818 21 2003 J N McClintick and H J Edenberg Effects of filtering by Present call on analysis of microarray experiments BMC Bioinformatics vol 7 pp 49 2006 G Finocchiaro P Parise S P Minardi M Alcalay and H Muller GenePicker replicate analysis of Affymetrix gene expression microarrays Bioinformatics vol 20 pp 3670 2 2004 S Dudoit R C Gentleman and J Quackenbush Open source s
17. l Detection are important parameters in making decisions of which genes are significant A recent study showed that using the number of Present calls as a threshold could ultimately eliminate unreliable genes while preserving the most significant genes in the result 9 A joint use of qualitative data Change calls and quantitative data fold change and signal mean ratios also demonstrated false positives were greatly reduced whereas the use of a single parameter has a false positive rate more than 30 10 Here we introduce a software tool called AffyMiner that uses both quantitative and qualitative data metrics for detecting differentially expressed genes in GeneChip data In addition AffyMiner has functions for associating gene annotation information and Gene Ontology GO descriptions with significant genes detected that provide better biological interpretations of the results 2 Software 2 1 Design 2 1 1 User requirements The requirements established from discussions with the users of our Microarray Core Facility over the past three years include e Making use of rich data metrics generated by the Affymetrix system e Providing flexibility for the user to choose different data metrics and different threshold values for filtering for differentially expressed genes e Incorporating statistical analyses for the selection of significant genes e Facilitating exploratory analyses such as clustering analysis e Incorporatin
18. m for analysis 2 4 Implementation Availability Installation and 2 4 1 Implementation AffyMiner was developed in the Microsoft Net platform and programmed in Visual Basic Net 2 4 2 System requirements The minimum requirements to run AffyMiner include a Pentium 3 or later computer 512 MB of Memory Windows 2000 or later operating system and NET Framework 2 0 or later 2 4 3 Installation To install AffyMiner double click on AffyMinerInstaller msi and follow the instructions AffyMiner requires NET Framework 2 0 or later installed on the computer which can be downloaded from our website at http bioinfo sry l awh unomaha edu affyminer Or Microsoft web site at http msdn microsoft com netframework 2 4 4 Availability AffyMiner is available for download at http bioinfo srvl awh unomaha edu affyminer The user manual can be found from our website as well 3 Case studies AffyMiner has been tested by multiple users and their feedback has been incorporated into its current features 15 16 In the following example we describe two case studies using AffyMiner to find differentially expressed genes one related to nutritional genomics and the other to cancer informatics 3 1 Inflammation and delipidation of white adipose tissue in mice Our group YX and MEF studied the gene expression changes in the retroperitoneal white adipose tissue RP WAT in mice fed trans 10 cis 12 conjugated linoleic acid
19. nd 4 remove the probe sets with change directions being down or p values above a threshold defined by the user Figure 2 The algorithm for detecting significantly down regulated genes is similar to the algorithm above but with differences as described below 1 eliminate the probe sets with signal Detection calls of Absent in the control samples instead of treatment samples 2 select the probe sets with signal Change calls of Decrease rather than Increase 3 eliminate the probe sets with Signal Log Ratios above instead of below a threshold and 4 remove the probe sets with change directions being up instead of down The algorithm for annotating genes is simple Probe set IDs appearing in both the gene annotation file and the gene list are used to link gene annotation information with quantitative and qualitative data of significant genes 2 2 2 GOTree The Gene Ontology GO Consortium produces structures of biological knowledge using a controlled vocabulary consisting of GO terms 12 GO Proceedings of the 40th Hawaii International Conference on System Sciences 2007 terms are organized into three general categories biological process molecular function and cellular component The terms within each category are linked in defined parent child relationships that reflect current biological knowledge All genes from different organisms are systematically associated with GO terms and these asso
20. ng various gene annotations AffyMiner has been used for the analysis of GeneChip data described in several publications and has been found to reduce the time and effort needed to compare data from multiple arrays and to interpret the results in terms of gene and cell functions 1 Introduction DNA microarrays are a powerful tool for monitoring the expression of tens of thousands of genes simultaneously 1 Affymetrix GeneChips are widely used microarrays with a collection of 11 20 probe pairs called a probe set that measures the expression of each transcript The probe pairs comprise a perfect match PM and a single base mismatch MM to the target mRNA region GeneChip microarrays use a statistical algorithm in Microarray Suite 5 0 MAS 5 0 Affymetrix to estimate the variance among probe pairs within a probe set and to compute an expression index that represents transcript abundance 2 The MAS 5 0 algorithm uses the One Step Tukey s Biweight Estimate to compute the Signal intensity for each probe set and employs the Wilcoxon signed rank test to assess the Detection calls and p values for a single array analysis 3 4 The algorithm uses normalization and scaling techniques to correct for variations between two arrays 5 The comparison analysis of two arrays results in data matrices such as Change p value Change and Signal Log Ratio In the case of replicate sample analysis the two sample statistical tests such as the Stud
21. oftware for the analysis of microarray data Biotechniques vol Suppl pp 45 51 2003 12 13 14 16 M A Harris J Clark A Ireland J Lomax M Ashburner R Foulger K Eilbeck S Lewis B Marshall C Mungall J Richter G M Rubin J A Blake C Bult M Dolan H Drabkin J T Eppig D P Hill L Ni M Ringwald R Balakrishnan J M Cherry K R Christie M C Costanzo S S Dwight S Engel D G Fisk J E Hirschman E L Hong R S Nash A Sethuraman C L Theesfeld D Botstein K Dolinsk B Feierbach T Berardini S Mundodi S Y Rhee R Apweiler D Barrell E Camon E Dimmer V Lee R Chisholm P Gaudet W Kibbe R Kishore E M Schwarz P Sternberg M Gwinn L Hannick J Wortman M Berriman V Wood N de la Cruz P Tonellato P Jaiswal T Seigfried and R White The Gene Ontology GO database and informatics resource Nucleic Acids Res vol 32 Database issue pp D258 61 2004 S Zhong L Tian C Li F Storch and W Wong Comparative Analysis of Gene Sets in the Gene Ontology Space under the Multiple Hypothesis Testing Framework Proc IEEE Comp Systems Bioinformatics vol 2004 pp 425 435 2004 S Zhong C Li and W H Wong ChipInfo Software for extracting gene annotation and gene ontology information for microarray analysis Nucleic Acids Res vol 31 pp 3483 6 2003 R Alvarez Venegas M Sadder A Hlavacka F Baluska Y
22. or example line 3 of the Gene Ontology tree as shown in Figure 8 indicates the node represents behavior in biological process with GO ID 7610 and 2 probe sets on the significant gene list associated with this GO term The tree can be expanded or clipped by clicking on the small square boxes A window displaying the Affymetrix IDs associated with the GO term will pop up when the number is right clicked File Expand Help Gene Ontology B biological_process GO 8150 253 behavior GO 7610 2 cellular process GO 9987 45 development GO 7275 40 growth GO 40007 1 physiological process GO 7582 233 regulation of biological process GO 50789 13 reproduction GO 3 14 molecular_function GO 3674 253 antioxidant activity GO 16209 1 binding GO 5488 77 carbohydrate binding GO 30246 3 sugar binding GO 5529 3 mm nnnm d Figure 8 An example Gene Ontology tree generated by AffyMiner 2 3 3 Interfaces to Cluster and GenMAPP Both Cluster and GenMAPP programs need to be downloaded and installed on the local computer see below for the system requirements of the computer Go to the websites http rana lbl gov EisenSoftware htm and http www genmapp org download asp to download Cluster and GenMAPP respectively In the main window clicking the button Set Path will set up the path to the corresponding program file Figure 3 Clicking the button Cluster or GenMAPP will run the progra
23. ray clase infile Figure 2 The algorithm for detecting up regulated genes 2 3 AffyMiner AffyMiner includes GeneFinder GOTree and interfaces to Cluster and GenMAPP as shown in the main window of AffyMiner Figure 3 Brief descriptions of AffyMiner and its modules are also available in this window To illustrate the functions of AffyMiner we will use Affymetrix Drosophila Genome 2 0 array data generated in the aging experiment with caloric restricted Drosophila kindly provided by Dr L Harshman at the University of Nebraska Lincoln AftyMiner An integrated tool developed for Affymetrs ene ses two fully functioning pregrans ere inde third party applications ste ana a designed for in Visualizing gene express pratenting biological pathways Download et Atte eew geneapp org doenioed as Figure 3 The main window of AffyMiner 2 3 1 GeneFinder GeneFinder has two programs Significant Genes for finding differentially expressed genes satisfying the user defined criteria and Annotation for linking gene annotation information with the gene list The Significant Genes program has interactive interfaces to set up parameters upload input files and define columns in the output table output respectively The parameter setting window contains three frames for setting up the number of replicates the direction of a robust change and the data metrics for detecting differentially express
24. re found between different methods 20 On the other hand all the ten genes validated by the quantitative RT PCR were found by AffyMiner which indicates that incorporating qualitative data metrics might increase the selectivity of detecting differentially expressed genes Table 1 Differentially expressed genes found by AffyMiner and RMA Genes Approach Up regulated Down regulated AffyMiner 1927 2162 RMA Bayesian 2530 9877 approach Common in both 1432 1514 3 2 A novel isoform of estrogen receptor o ER a36 and breast cancer Understanding the molecular mechanisms by which estrogens drive breast cancer development has long been a challenging issue Numerous studies demonstrate that the human estrogen receptor a hER a66 contributes to development of human breast cancer and is a critical determinant in assessing prognosis as well as designing treatment strategies of breast cancer Recently we discovered a novel variant of hER a66 termed hER a36 21 The hER a36 lacks both transcriptional transactivation domains AF 1 and AF 2 but functions to inhibit the ligand dependent and ligand independent transactivation activities of hER a66 and hER B To further investigate the molecular mechanisms by which hER a36 functions during breast cancer development we conducted microarray experiments to profile estrogen responsive genes in the ER positive human breast cancer line MCF7 that highly express recombinant hER a36 as contrasted
25. y Significant Genes program in GeneFinder The Annotation program links the annotation information with gene lists and generates a user defined table with quantitative data such as signal log ratio and annotation information The NetAffx annotation file needs to be in the CSV Comma Separated Value format which can be downloaded from the Affymetrix website http www affymetrix com The gene list input file can be the result generated by Significant Genes or any text file with a column corresponding to Affymetrix probe set IDs Once these two files are uploaded the columns in the output table can be chosen from the list box interface E GeneFinder Paamete Settings Select Columns Gene Arentation Rexi Select Data Fie itab cbr vated Inmet C Documents and Settings quceinaDerktoo May Mining Programi S TA a t test for ater Reewmar Select cokumns Ot gt Ti_Signa 003 T _Sigral O05 gt T3_ Signal O07 C1 Signal 000 gt Cl_Detecton 18 gt C2_Signal OIG gt C2_Detection h 19 cohsrdt j tt gt OT Sigan 2 Signal Changed coksnnis needed 2 gt C3 Detector 49 Ti va C Derg 2 Tenatmert ve Conio Change Direction OIG gt TI ws C2 Change An o O18 gt Ti ve C3 Charge 020 gt T2 ws Cl Chance 02 3 T2 va C2 Charge 1 Siga Tiea Detection 3 coluesnis needed UW gt 11_Detectar 04 gt T2_Detection U6 gt 13_Detechon 3 Signa Raol columnis needed O13 gt T1 ws Cl Signal Log Ratio UNS gt 11 we C2
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