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1. 2 eieeeee eene 12 20 e COSE iv ata min usd Me OMM MI aden ME EI IC NOME RUEE 13 3 7 REVIEWING the Fesulls s 2 rip bcd pdt beedercd eite a a erita 14 SiG csusdtetueer emirviata rent ci cevE eer but oa ober eee bx mL du sso PSU UEL UE 15 3 9 REVIGWING SPEC d 2 16 LOr r E 18 4 R viewindg the Gata WIEN PCA ssim mrin deor EREREETE HEY FER EEEVEIE MPEYFIR arte 19 4 1 Performing Principal Components Analysis cccscscsececeeseceeeeseeeeeeseeeeeuseeeeauseeseausenetensenes 19 42 Understanding the display 1 20 4 5 Interacting With the 21 qo Displaying oue POS ENTE IN FOVERE NEIN KMUDEI E UR IM EUR QUE 22 Hose 295515095 Ont O Forte ra Rast Fas ears Gs s DN Eric a P ON Er E I ME LE ILI SUI 22 4 3 3 Selecting and displaying the behavior of variables eeeeeeeeeeee eee 23 44 Interpreting tle FESUINS 5 ects Deos S vetri neat nb E a oes nar bas ce oec cone ape c M uos en LEE 25 OUI Vote ome 0 0 um D I DU MM AI DU MEI MI ME 25 5 Unsupervised processing of LCMS 1 oou LR ree L eue abso Lea epo eae ence P CES 27 Bel AMPON dala isses dara Rn el ceu Nc ies EXM EM MEC E E M E RM 27 5 2 JAssigninadroeups and 5 30 5 3 Performing PCA and interpreting the results leeseseeeeseeennnen nnne 32 5 3 1 Principal Component Variable Grouping Utility eeeeeee nennen
2. C5 M5 C8 MS Sample by index 3 9 Reviewing spectra 1 Sortthe table in ascending p value order and select the first row to generate the profile graph using the plot profile icon 2 Click and drag in the graph so that a few samples on both sides of the sharp intensity change are selected MarkerView 1 2 1 Software User Manual 16 Revision February 2010 973 56 DER e fu dh A XA ES Compare Ato C nl 5 0 Indes Peak Mame miz Ret Time Use t value p value r5 973 56 973 5625 N A Isotope 36 69 5 1325e 18 93487018 8 M7e3 0 1302 ESEE ZSAE 94 v 5 307 4728 N A Isotope v 5 8315e 16 3 E D oR d HR 9 Ed Q E3 373 56 A2 MS 1 12d Zoom Selection Display Show tus i C3 MS Don t Use Peaks For Analysis ited 5 CE MS 10d C10 MS 1 t2d Set Group For Peaks Add Active Peak to Interest List A Sa L2 5 1 t2d L5 M5 1 t2d L8 M5 1 r2d a mple by Indes 3 Right click in the plot and select Spectra from the Show submenu A progress bar will appear while the program locates the raw data files and extracts the spectra followed by a graph showing the spectra MarkerView 1 2 1 Software User Manual 17 Revision February 2010 Spectra Test 37356 Spectra 1 5 23 86 E 5pectrumfrom 0 5 1 r2d H5 700 BUT 7 Spectrum from amp 1
3. 0 0 0 1 3 PC Score PC1 Loading 105 0 10 8 22 2 Hide all panes except the loadings plot by clicking the Hide pane button select one of the variables with the largest negative PC2 loading e g 359 1 10 8 and generate the profile plot 3 Change the sorting to Group Order As we ve seen before these are the variables that demonstrate the diurnal variation Some other families of variables are also apparent and are marked A F in the figure below oadings for PC1 62 4 versus PC 18 6 Pareto LCMS Saved EIE X a8 EE Loadings for PC1 62 4 2 versus PEZ 18 5 4 Pareto 5 Default Isotope 381 2 1 ge 268 g 399 243 E Manoizotepic F 321 2 E 236 d D 343 1 15 4 33 3 333 2711 7 323 di 4002 129 397 2331 1 D 161 278 1 12 8 px ees 12 7 206 PC2 Loading amp 550 1121 3231 337 2 715 4 313 100 180140 8 3931 10 8 345 i 105 0 10 8 22 O06 O07 08 0 09 010 011 012 013 O14 FC1 Loading 8 345 393 1710 8 Rat3 D Bh blk Rat D Bh Blk Response vinpo 4 0 15 Hat vinpo A y Rat2 16 24h_vinpo A RHat3 8 15h blk A 0 8 Rat Bh vinpo Hat3 8 TEh vinpa 5 U Bh blk 4 Sample by group MarkerView 1 2 1 Software User Manual 41 Revision February 2010 4 As you click on other variable in the loadings plot the profile graph will update to display
4. 5995 Hm i seriasgon rs 0 9 mz foe ss amp zrzamg seis i25 I 8 55 Qe AIC from LEMS sample 2 Ratl_O 8h_vinpo A TOF MS 80 1000 383 22 7 0 0 Da 300 Intensity 12 282 12 379 12 334 11 941 Rive 13458 43 596 ee 11371 fi jg 12072 E hasar leg 11 462 4159 11 683 17 89 11 4 11 6 11 8 j 12 4 Time min The system will generate the extracted ion chromatogram XIC for a small mass window around the selected m z value the region between the blue arrows in the x axis indicates the range for the peak selected In this case it is clear that not only are the peaks at 12 35 and 13 1 min correct but there is an additional peak at ca 12 6 min that was merged with the peak at 12 35 min 9 Click the Link to Table button at the top of the chromatogram pane and select another row in the variable table e g one of the rows for the peaks with m z 381 2 and the chromatogram will update to show the behavior of this variable While it may be possible to find parameters that separate closely eluting peaks in this case a retention time tolerance of 0 1 min will allow the peak at 12 6 to be retained this may not be wise when there are several samples since small retention time shifts between the runs may cause different peaks to be aligned In complex samples it is definitely an advantage to introduce an in
5. Group Symbol pui o o x EMEN fe e MEN ems e Ll mm pe OK Cancel Select the first empty cell in the Sample Group column and enter A Enter C in the Sample Group cell in the next row You may change the shape size and color of the symbol by clicking in the appropriate cell and making a selection from the drop down menu Click in the color cell for the C row just added and select the red color All graphs and plots that show samples will now use filled blue circles for group A and filled red circles for group C Note that there are five special categories Default Excluded Selected Monoisotopic and Isotope that are used when no other symbol is defined for excluded samples and variables when particular samples are selected or to indicate isotope peaks respectively You cannot change the names of these symbols but you can edit the shape size and color MarkerView 1 2 1 Software User Manual 12 Revision February 2010 3 6 Performing a t test The t test is applied to every variable in the table and determines if the mean for each group is significantly different given the standard deviation and the number of samples 1 From the Analyze menu select Compare Groups with t Test or click on the t test button in the toolbar below the menu bar t IE MarkerView File Edit View EPIS Window Help GE E Aa ul _ Perform Pca Cirle Loca Chec
6. set a ja s ww msw ws Wes o oss use Amer 708 1884 N 0 30506 2 2421 O 000e0 ase Log Fold Change versus p value Far amp ta C 1833 30 Default RT 1290 66 el i s dins E v 3 7 4 M aonoisotapic SE Selected me abs 77 0 s 40 1001 51 mag 55a 47 2090 88 213435 A ipo 16 1320 68 1675 81 pu 73 223812 xe 2115 03 2141 00 Eo xp 1831 48 3642 60 oe 1 0 0 5 10 Log Fold Change 5 Right click in the table without making any other selection select Use ONLY Selected Peaks and perform a PCA analysis Loadings for PC1 89 5 versus PC 6 4 4 Pareto Saved Sample Name Sample ID Index Peak Mame m z Ret Time A1 MS 1 02d 705 38 705 3789 N A Mon A2 M5_1 t2d ll 708 38 708 3851 N Isatc sv g Ed eet mE Scores for PET 83 5 versus PC 6 4 x Pareto Loadings for PET 83 5 versus PC 6 4 Pareto ATO_MS_1 ted 1296 65 Default Isotope L8 MS_1 ted j Monoeisatopic Ab M5_1 t2d l 1297 69 5 M5_1 t2d 304 7 AB MS T r2d 1238 63 L4 MS 1 t2d 35 47 1570 68 1 10 MS 1 r2d A M T ted PC Score L3 MS 1 e L5 MS Ce MS 1 t2d FL Loading A2 M5 1 2093 10 Lr Ah 5 1 2d cdd d 1360 62 feces ed A7 MS 142d 1553 68 amp L3 MS 1 t2d As MS 400 200 0 FL1 Score FL1 Loading MarkerView 1 2 1 Software User Manual 67 Revision Febr
7. Select the unwanted variables right click and select Don t Use Selected Peaks 5 You can also select and use any variable groups s For example to use only the monoisotopic peaks this can also be performed directly from the peaks table right click in the table and chose Select Peaks For Group MarkerView 1 2 1 Software User Manual 60 Revision February 2010 Q How Index Peak Mame miz Ret Time Group Use Change Mono Mass Ma umm in o es Dp oo fos p s eoz sumo zs os d jt 8 8 830513 8 90 6161 61 MO 1 34 ect Pea ar Mis ing Peak Names 0 2 2 Bon fo oo e fe ama esa nz o 1 O 8 995 on m pe ea e re O a ws eo 6 The resulting dialog shows all the assigned variable groups select Monoisotopic and click OK Group Name Please enter the desired group name Mone Mone Isotope Moanoigata p Ic 7 This automatically selects all the peaks assigned to the Monoisotopic group right click in the table select Use ONLY Selected Peaks and perform a PCA analysis The resulting display is similar to those obtained earlier but with a much simplified loadings plot since there are far fewer variables 2 oadings for PC1 61 5 versus PC2 18 2 Pareto LEMS Saved sample ID idi Indes Feak Mame m z Ret Time LEMS Datawit sa 1 91 0511 13 gt z puman 74 Wy gt
8. e Click the arrow in the top left corner of the graph to shrink the title display to a single line reflecting the active front and labeled trace The active trace can be changed by clicking on another trace in the graph and all titles redisplayed clicking the arrow again e Click on the top border of the pane containing the display and drag the frame upwards to enlarge the size of the pane The cursor will change to a resizing tool when correctly positioned over the border e Click on the magnifying glass icon in the pane header This will cause the display to switch to a mode where each pane is displayed on a separate tabbed page Clicking on a different tab will change the active display and the process can be reversed by clicking on the magnifying glass again MarkerView 1 2 1 Software User Manual 24 Revision February 2010 5 In the loadings plot select another variable by clicking on it The sample graph will update but the selection rectangles will remain in place Click in one of the selections and choose Spectra from the Show submenu while holding the shift key down the spectrum graph will update to show the raw data peak for the new variable Since these are spectral data and the spectra have already been retrieved from the files the display will update much faster If the shift key is not down a new spectrum pane will be generated using data newly retrieved from the files 6 Remove the graph and profile pl
9. 3 In the PCA dialog select None for the Weighting and Pareto for the Scaling and click OK MarkerView 1 2 1 Software User Manual 32 Revision February 2010 PCA Options FPCA Preprocessing Weighting Mone Scaling Pareto Perform PCA DA supervised Samples to Keep Remove samples marked as not used Cancel The resulting display will show the scores and loading in both tabular and graphical form as described in detail section 4 2 4 Click the magnifying glass button 4 in the scores plot so that it is easier to examine Cy IE Scores for PC1 56 5 versus PC 17 7 Pareto LEMS Saved Scores Pareto LEMS Saved Loadings Pareto LEMS Saved Scores for PC1 56 5 versus FC 17 2 x Pareto LEMS Saved Loadings for PC1 56 5 4 P mm 8 E Scores for PC1 56 5 x versus PC2 17 2 X Pareto Fate 8 I5h vinpo A E Rats 16 24h_vinpo 3 Ratl_16 24h vinpo 5 Rat 15 24h vinpo 4 Hat 8 15h vinpa m 15 24h blk 4 Rat B8 1Eh bik A Rat3 BiTEh vinpo A Hat3 15 24h blk A 2 Score Hat2 8 15h 0 0 vinpo 4 Rats D Bh vinpa 4 Ratz 15 24h Blk A Hatl 8 15h blk amp D 8h vinpo 4 Rat3 D Bh blk A Rat2 D 8h bik A CH Rat 0 8h Blk A 40 60 PCT Score It is clear that PC1 ca 56 5 of the variance separates the pre dose samples open squares from the post dose closed circles wit
10. 31 Revision February 2010 Rat3 16 24h vinpo A Rati 0 8h blk A Rat2 0 8h blk A Blanki Rat3 0 8h blk A Rati 8 16h blk A Rat2_8 16h_ blk A Blank2 Rat3_8 16h_ blk A Rati_16 24h_ blk A Rat2_16 24h_ blk A Blank3 Rat3_16 24h_ blk A The finished sample table will look like Sample ID Group Use Acq Time Scale Factor HT Correction 0 8h_vinpo A LCMS Data wiff sa 1 11 10 2004 2 12 P 1 0008 Mone H r sstsece k eee 7 ure S Rat Beh vino A IMS Dates 2 Iv T T0 2601306P 109080 Noe Lr e Sinka CONS Dates Berke I Aa saae 1 00060 Now ee atte 28h Bk A COMB Datawi ca s fe I Aa sane 100060 Noe S Ra Beh vinpo A COMB Dates Hu Ra ber bk A CONS Dates I WAROLESTP T0 Now HW Fean A CONS Dass s A avaa e 1090 Noe Hz e Fesa Bk A CONS Dates rk A Aa TAP T0008 Noe 14 Select Save As from the File menu and save the imported data in the LCMS Data folder with the name LCMS Saved overwriting the file if it already exists This will save the imported data and the assigned groups so they can be easily retrieved in future 5 3 Performing PCA and interpreting the results 1 Click the trash can icon in the sample table pane to close it 2 Click the PCA button or select Perform PCA from the Analyze menu
11. BB m C E i Emel A EE Scores for PC 67 5 E versus PEA 18 2 x Pareto Loadings for PC1 61 5 X versus PC 18 2 X Pareto hat2 8 15h vinpa A 91 1 11 3 10 TT ie 387 3 11 3 384 6 cx 1541 11 111 3021 12 4 268 Rat 1524 vinpo par B AEh vinpa A 1 353 3 20 7 340 323 2 13 0 301 Rat 16 24h vinpo A 248 a 2751 125 224 Raa aih vinpo O 203 266 1 12 8 a 335 3 0 2 1 333 asd 280 1 12 7 238 Ratl 8160 5 Rat3 Bh vinpa A 1 B 24h_bik A 5 T ppm Rat3 0 8 blk A Ratk 38 ial ic PLZ Score FL Loading 338 12 1 321 383 2 15 3 375 Hatl Bh winpa 4 3581 10 8 345 sony ang 100 Rat2 08h blk A 4 105 0 10 8 22 Rat Sh Blk 3 AO Ll 0 0 0 1 0 2 0 3 4 PL1 Score PC1 Loading 0 MarkerView 1 2 1 Software User Manual 61 Revision February 2010 8 The Peak Info table also contains a column for mass defect the difference between the measured m z and the nearest integer Simple metabolic changes made to xenobiotics tend to shift the m z value without substantially altering the defect so looking for compounds with similar mass defects to the parent drug can help identify metabolites The table can be used to filter compounds based on their mass defects Mass defect can be expressed in two ways Relative to the nearest integer In this case some values will hav
12. r Filtering Period 1 Experiment 1 Minimum required response 1 00 0 Find spectral peaks in profile spectra ODD SD Maximum number of peaks Mass tolerance 200 ppm C Bin spectrum Bin size E Subtraction half wiridcus 0 bins Cancel 5 When the import operation has finished the data table will be displayed Use exclusion list Cel MarkerView 1 2 1 Software User Manual 7 Revision February 2010 JB Peak Mame m z Het Time Group Use A M5 1 t2d A M5 1 tzd 700 15 700 1481 6 020e1 0 000e0 me ma p ea T ma ores wa __ e oo To pm ma wm a a 58e ma ore 09e 13tie me ers pa Eie uzee pm EE 702 93 702 9307 Honoisotopic 702 04 702 0412 INA 5 235e1 0 000e0 mass eseja 8 umo pm The table contains a row for each variable and a column for every sample Variables are identified by a peak name m z and retention time since these data are from mass spectra alone there is no retention time available and the peak name is simply the m z value The table also contains a column which allows the variables to be assigned to a particular group When the application reads MS or LCMS data it attempts to determine the charge state of each variable based on the spacing of the isotope peaks and assigns it to one of tw
13. PCT Loading PL Score PC Loading The 0 8 hour samples pre and post dose appear to be separated from the rest of the samples with negative PC1 scores and the majority of the remaining post dose samples have high positive PC1 scores This suggests that the variables with high positive PC1 loadings will be more intense in the remaining 8 16 and 16 24 post dose samples 10 Select the variables with the highest positive PC1 loadings and plot the profiles 7 de Hu 9s Ba e 91 1 11 3 10 100 Hat3 15 24h vinpa 4 Rat3_8 16h_blk 4 194 17117 5 111 vino 4 Rat 8 16h blk i 387 2 11 3 364 i po E iw E Hat3 15 5 C EL BM we atl gre Bk A gt 1 20 26 A z Bara p ah Fate D Bh winpa 5 Hat3 8 1 Bh npa 4 0 blk 4 Hatz 0 5 blk Fats 16 24 blk A Sample by group These variables appear in most samples and the behavior is modified in those obtained post dose being somewhat higher in the 8 16 and 16 24 hour samples and perhaps lower in the 0 8 hour samples Since they all have the same retention time 11 3 min they are likely related m z 387 is probably a dimer 2M H of the ion at 194 MH and 91 1 a fragment These may be variables that we want to process further so we will transfer them to the interest list 11 The profile graph s context menu allows some flexibility in editing the variables displayed and adding them to the
14. longer part of the display Zoom the graph if necessary by dragging in the horizontal axis Sort the table by ascending p value as before and note that the first value is now much lower If Remove samples marked as not used is unchecked in the t test dialog excluded samples will not be used to calculate the t test values but will be retained in the displays This provides a way to classify unknown samples i e compare them to known samples 5 Sort the table by ascending t value select the first row and display the profile graph The variables with negative t values seem to be higher in the C samples than in the A group i e both groups appear to contain unique variables not just the samples spiked with calibrant In this case it seems likely that this is an experimental variation for example suppression of some peaks by the spiked compounds so that they appear to be less intense MarkerView 1 2 1 Software User Manual 15 Revision February 2010 TE 1129 53 lae elo ae Compare Ato C nl 9 n2 10 Index Peak Mame m z Het Time tyalue p value 1049 1123 53 1123525 NA Isatape 10 75 5 20308 3 4 22082 EBEE zz D 73002 ASe 0 mn 1342 002 H A v 3 2334e 8 O 000e0 1129 53 C1MS_1t2d ay C8 MS 1 t2d AE MS 12d C7 MS 12d A2 MS 142d AB MS i2d C3 MS 12d C10 MS 12d C8 MS 1 t2d ATO MS _1 t2d AS MS 1 t2d Af MS l t2d Response A2 M5 A5 M5_1t2d 8 M5_1t2d C2 M5
15. of the total even though the plot visually suggests it is more significant e One of the blue samples A9 MS 1 t2d is more similar to group C red than it is to the other members of group A This is the sample that was also identified as an outlier during the t test section 3 7 and subsequently excluded It is still included here because the data table was saved before the sample was excluded The loadings table 3 also contains a column for each PC but in this case the rows correspond to variables and the values in the cells indicate the loading for the various PC s The loadings plot 4 displays a point for each variable colored according to the groups assigned as the data is imported and the symbols assigned to the default groups see section 3 5 as illustrated monoisotopic peaks are represented by large green circles other isotope peaks by small green circles and unassigned peaks are blue Coloring the variables in this way allows you to quickly determine their importance The loadings plot has some interesting features e The vast majority of the points are clustered around the origin i e they have small loadings and contribute little to either PC1 or PC2 e Anumber of variables have large positive values on PC1 and PC2 Since one group of samples A is separated because it has large positive PC1 scores as shown by the clustering of the group A samples in the Scores plot these variables with large positive PC1 loadings are r
16. sort copy etc as with other tables Unlike the exclusion list there is only one interest list m z Het Time Group Change Mona Mass Excl Comment 31 0511 11 29 Manaizatapic 1 90 0433 Potentially interesting P 8 e ea E The interest list contains other peak metrics such as the assigned variable group charge state calculated mass cf m z etc The calculated mass is obtained from m z and charge assuming that protons are gained in positive mode and lost in negative mode isotope peaks have their own mass not that of the monoisotopic peak Since displays are not removed as new ones are generated it is possible to back up to an earlier display and continue exploring the data Close the current window and the previous window including the selection region used to exclude variables will be revealed Right click in the selection rectangle and select Use Selected Peaks for Subsequent PCA to restore those variables If you save the data probably with a different file name the excluded samples and variables are remembered so that the exclusion process does not need to be repeated 5 5 Using Principal Components Analysis Discriminant Analysis PCA DA Discriminant analysis DA like the t test is a supervised method that is used to find differences between known groups The MarkerView Software allows DA to be combined with PCA by clicking on the Perform PCA DA supervised checkbox in the PCA Options dialog box s
17. 1 4 066e1 BBB 3252 12388 325 1828 e dh l er BF 338 1 12 1 B7 338 0813 8 557e1 TETUER Plot Peaks for UNE eo ss azn a 23 Plot Profle for Peck 173762 8 360125 80 340 1048 Show Spedra 341 17124 341 1029 40 Peak List Spect Ed Cont i 35327131183 a 139 sosedni 4 362 17125 34 362 0858 First IDA Product Spectrum gt 7 368 2127 85 368 1613 Select Peaks For Matching Peak Names Best IDA Product Spectrum 5 s 33152708 3731165 PITER ARS ETAP All IDA Product Spectra Set Group for Selected Peaks i 3771 12 8 87 377 1485 p e uu He Add Selected Peaks to Interest List poer EHE 378 1 12 8 B8 378 1420 L aemp 00000000 p A 6 710e0 gs 5 8 134 ac 399 FAZIO a a 1 5 EE i03 998271311103 399 1967 fiaa Monosotax 7 1 20602 pe 492731094 um 1272 MarkerView 1 2 1 Software User Manual 54 Revision February 2010 Seles Peak Mame m z Het Time D Bh vinpa 4 3b8 2 12 35 365 1613 12 66 5 41221 88 s sina ris sms pros E eer t s srumzsen sre was Mesa fe eraa sum raat eed S 8 f ems sure a pu uo suse ue
18. 3 21 1 407 PL Score PC Loading 4 149 0 24 5 B7 Fla D Bh rinpec amp si 1490 41 70 qne SHANA i f 2551 7128 209 E blk A ene EEDS Rat 0 8 blk A 2 Ure ch D 8h blk A 0 gt 180 1 10 8 100 E 3381 12 1 321 100 O1 00 01 02 PCT Score PC1 Loading Explore the data using the techniques and tools described in section 5 and confirm that while the scores and loadings plots look different and the amount of variance explained by the principal components is also different the conclusions drawn earlier still apply 6 3 2 Filtering data In many case the data will contain variables that are suspect e g too small artifacts e g arising from contamination or not wanted in the analysis This section briefly describes some methods of identifying and removing such peaks 1 Close all open windows re open the saved LCMS data and perform a PCA analysis 2 One useful way to filter data is to exclude variables that do not appear in a certain minimum number of samples This is particularly relevant in data such as this since there are three samples for each time point and dose so variables appearing in just one are likely noise individual variation or misaligned Select Make Peaks Appearing in Few Samples Unused from the Analyze menu and select 2 in the combo box in the resulting dialog MarkerView 1 2 1 Software User Manual 59 Revision February 2010 Number of Required
19. 37 5 4 Working with the excluded and interest lists esee 40 5 5 Using Principal Components Analysis Discriminant Analysis PCA DA 46 SOME IU ter 47 elg cm E 49 6 1 Generating and importing Peaks files isses eren nnn 49 6 1 1 Generating peak list files 2 nnn nnn nnn nnn nnn 49 6 12 Importing peak ISU THOS ssec dia ev erat ex aeu ERO TH DoD ER EVCR VES QU ERN DS ea eine iut 50 6 2 Reviewing peak finder PerfOrMance cccecscsrsscscsrsecscuusececursesecaususeearsuseeaususeeararseeavanses 51 6 3 Aligning normalizing and filtering data cccceceeseseceeeeseceeeesecseeeseeseseseeeeaesetstansesstensenas 57 6 3 1 Altgoing and mormalizilt sas sica esses ie decus rz E ra 57 6 3 2 Filtering data use vAxE Den EEN ERO vanes RI SO IE Pe t ceu rias rm ur RD UP aU MU ME 59 6 4 Selecting discriminating t test variables eeeeeeeeseeeee nennen nennen nenne 64 6 5 Combining ttestand PCA asaru entes rexie eernc i dE iste utro Fe ua EU Uu sub viae dE ExUci Sui Eee UE RESUE f 68 MarkerView 1 2 1 Software User Manual 3 Revision February 2010 2 Introduction and typical workflow The MarkerView Software is designed to allow the data from several samples to be compared so that differences can be identified typical applications include metabolomics biomarker discovery metabolite identification impurity profiling etc Th
20. Change Here the x axis is the log of the fold change the ratio of the means of the two groups and the y axis is the p value Variables that appear in one group but not in the other i e that have an infinite fold change are drawn slightly beyond the real values 819 12 on the left and 774 18 on the right for example Since small p values indicate variables that distinguish the groups well the most significant are those that have low p values but high fold changes those that have high p values and low fold changes are not useful If the variables are colored according to their isotopic status you can select the monoisotopic peaks or ignore those that are unassigned 3 Click on a variable with a large positive change to select it in the t test results table and in that table click the Plot Profile button to get the following MarkerView 1 2 1 Software User Manual 65 Revision February 2010 3E 708 19 Saved fu fu Ed A 8 Es ES Compare Ato C nl 9 n2 10 Index Peak Mame m z Ret Time t value us value Mean 1 Mean 2 708 19 708 1884 H A 1 06 2 02461 0 000e0 E 8 s si E e jme ques we mea we ums uem ENNSCNNSL NENEKC IL NN a D NECS C AO As MB A2 5 1 t2d Ah MS AS M5 1 t2d C2 M5 1 t2d L5 M5 C8 M5 1 r2d Sample bu index 1 5 8 Log Fold Change versus p value For 4 tat 667 64 1014 98 500 79 B Default 782 11 dk M Selected
21. Samples Make peaks appearing in fewer than 2 samples with response less than or equal to 0 0 unused gt In the loadings plot a number of variables close to the origin will now be drawn as open circles to show they have been excluded you may need to zoom to see this Since these are small peaks a new PCA analysis will show little change 3 Close all windows except the peaks table and select Show Peak Info from the View menu the peak info table appears in the lower part of the window Peak Info LCMS Saved Peak Name m z Ret Time Group Use Fatl_O Sh_vinpo Ratl 0 Fatl_8 16h_vinpo Ratl 8 16 Ratl 16 24h vinpo Rati 77 81 1 13 3 1 13 32 0 000e0 amea ae ws Ta 0 IN 007 ms 171200 00606 e s sen 517960 6000550 eos us conned 60000 eo we TAS ar 6 p b 1 30 6 11 3 8 90 6161 11 34 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 0 000e 91 1 13 2 3 91 0507 8 1 533e0 0 3 175e0 0 000e0 1 512e1 16 gt INE swissmy sues mss l 0905 tren oos Btreh 0099 9 Ec sues wars i 0995 tren 0095 tte 00020 2207 amp m z Ret Time Group Use Charge Mono Mass Mass Defect Mean Median Sigma ASD Min 5 4376 2 0 000e0 2 307e 1 4 243e2 0 000e0 1 1 A zz arias sse
22. can also have a significant effect but will have no effect if set to values that are less than those used to import the peaks initially 6 2 Reviewing peak finder performance Peak finding is a critical part of the program and it is important to set the parameters correctly to generate the best results This is invariably a compromise since including small noise peaks will add no value to the calculations and may confuse the displays while small real peaks may be critical to the separation desired A good way to evaluate the peak finder is to import a small range of the data from a single sample and observe the behavior using chromatograms and contour plots as described in this section From the File menu select Import lt LC MS Data from wiff Locate the LCMS data folder expand the LCMS Data wiff file by clicking on the sign adjacent to the file name drag the second sample to the right side of the display Selected and click OK MarkerView 1 2 1 Software User Manual 51 Revision February 2010 1 2 S Select Samples Project Browse C Program Fileg amp B 5ciessM arkerview 1 045 ample Data Available Selected LEMS Data wilf E Sample Data g Fiatl D Bh vinpo A f LEMS Data g Ratl 0 Sh_ bik 4 LEMS Data witt g Ratl 8 10 vinpo A g Rat 0 0 vinpo A g Ratl 8 15h blk A g Rat 15 24h vinpo 4 g Rat 15 24h blk 4 g Raz D 8h vinpo 5 g Raz D 8h blk A Ratz 0 10 vinpa A g
23. due to metabolites of vinpocetin 1 From the display which is the result of performing PCA with some variables excluded you may need to regenerate this plot if you closed it in the previous section select a region of the loadings plot containing variables in families 1 2 3 and 7 earlier figure and extending to a PC1 loading of about 0 15 right click and select Zoom Selection MarkerView 1 2 1 Software User Manual 40 Revision February 2010 oadings for PC1 62 4 versus PC 18 6 Pareto LEMS Saved Sele Sample Mame Sample ID amp Indes Feak Mame m z Het Time Fiatl_O Sh_vinpo 4 LEMS za aye 81 1 13 3 1 81 0637 13 32 1 5 8 3 Scores for PC 62 4 X versus PC2 18 6 x Pareto Loadings for PCT 62 4 X versus PEZ 18 5 X Pareto Fat 8 15h vinpo A Default 5 10 1 1 Isotope 384 1 3 367121 4 Monoisotonicl 38 0 944 zoom Selection Show Selected Points in Table 4 236 1 Hz Plot Profiles For Selected Peaks Fats 16 24h_vinpo A Display Ratl_16 24H blk Don t Use Selected Peaks For Subsequent PEA Use Selected Peaks For Subsequent PCA Use ONLY Selected Peaks For Subsequent PCA PC Score PC2 Loading Hat D B8h vinpa 4 Add Selected Peaks to Interest List I L RHat3 D Hh blk 4 4 rh Ratz 0 8 Blk A 1 90 10 8 100 i 143 B3 Fatt Bh blk A
24. ne Pr waar W oor 50 80000 06 426992 1 5 s ema e es W el WA ooo 434804 00095 7 47600 0 a f fesa e was __ WA WA oos Sease 0090 259602 oen rece s s emo moss ws o Ws NA owes 506061 ooo 126360 248602 00 3594e t s ema eos wi I Ws el NA oos 54e 000000 seite 177 lt 2 00 30 6 s tr mumsm eoe ns a W el WA oos 14er 13er sezen Corder 1 1960 96 Te s s omo mss na Vs NA 939 7452ez 00095 31621 126ez 0090 s s simzg sor w s V el WA oos iee 577955 138261 viesez 00000 135 15 Hr m famm sow ws Ri W amp WA oos z49eT 000000 sisrez 00 2090 Hz pe amna sues wis W WA oos oreo 00005 17z7eb 00 5959 t Hs n ama sos wzr wee stows oos 50 154i Teret rre rreo ree m u emana wows nz TR VA el WA oos 35er ooo 12cez 0900 53596 This table contains detailed metrics for each of the variables in the data these are explained in the Reference Manual and can be used to filter the variables 4 Another way to exclude peaks appearing in only a few samples is as follows select the Samples gt column sort in ascending order
25. not exceed 5 the value that we initially used as a threshold when importing the data In addition to the intensity there are a number of other reasons why small peaks may be rejected by the peak finder for example MarkerView 1 2 1 Software User Manual 56 Revision February 2010 The mass peak does not appear in enough contiguous scans less than the Minimum RT peak width defined when importing the data The m z width of the peak is less than the Minimum spectral peak width If Subtraction offset was checked for any given peak there may be another peak ahead of it by the offset value used when subtracted this may cause the target peak to be less than the specified intensity thresholds The operation of the peak finder is described in detail in the reference manual and you are encouraged to experiment with the parameters and observe the results using the tools and approach described here 6 3 JAligning normalizing and filtering data 6 3 1 Aligning and normalizing As indicated in the previous section aligning peaks is essential for best performance If you have added one or more internal standards to the samples you may specify these in the RT Correction and Normalization sub dialogs of the Alignment amp Filtering dialog box The tolerances in these dialogs refer to the windows used to locate the internal standards and are typically wider than the values used to actually align the data RT Correction Correct
26. oe Ag Ny Je C2 MS lid NU S iMd C8 MS L 2d C9 MS 1 02 E F b 0 ge A2 M5 lt2d AS MS AS MS C1 MS 1d C4 MS 1d C MS C10 MS Sample bu index It is clear from the display that these two variables are present in group A samples at a high level and only at a low level in group C samples The excluded sample A9 MS 1 t2d also shows a low level consistent with group C The intensity for the peak at mass 1297 69 is lower than the peak at 1296 69 in all samples as expected for a C isotope peak at this mass and suggested by the scores 3 Inthe loadings plot click on the point for the variable 904 47 and the display will update to show the profile of this peak MarkerView 1 2 1 Software User Manual 23 Revision February 2010 This is the default behavior when there is an active graph and a single point is selected if you hold the shift key down when clicking on a new variable point the profile for the new variable will be added to the existing display If you make a selection that includes several points you will need to click the Plot profile button or right click and choose Plot Profiles for Selected Peaks to generate the display By default a new profile graph will be generated if you hold the shift key down while generating the display the existing plot will be replaced 4 In the Profile Plot select a region containing a few samples from the A group hold the sh
27. the selected variable Check that Group Order is selected for sorting and explore the behavior of other variables such as those indicated with circles in the above figure note that the intensity pattern changes as you move counter clockwise as shown below This figure was generated by drawing selection rectangles around the variables rather than clicking on them and plotting the profiles The panes were arranged by dragging the moving truck icon 279 1 15 1 233 LCMS Saved PIE 8 LESER at O 6h_vinpao amp l Har 8 15h vinpo 5 Hesponse Response Hat3 8 16h_vinpo A Rat 0 16 blk Hat3 8 Ibh vinpa A Hatz B 1bh blk A Sample by group Sample bu group C ES EJ Hat3 D 8h vinpo 4 B Hat 8 T1Eh 3 5 1Eh vinpa 4 Hesponse n pus i e CL bd n Lc Hat3 8 1bh vinpo A Rat 8 16h_bik Hat3 8 1bh vinpa A Hatz B 1bh blk A Sample by group Sample bu group 238 1 12 8 158 Rata D 8h vinpo 5 Hat3 B IEh vinpa amp Rat3 1E 24h vinpa A Response Response Hat2 B8 IEh vinpa 4 D Hat3 8 1bh vinpoa A Fat B8 l1bh blk Hat3 8 1bh vinpa A Hatz B l1bh blk A Sample by group Sample bu group The different families illustrate the different kinetics for different metabolites Those lying along line A occur only in the 0 8 hour samples while the relative amounts in the 8 16 and 16 4 hour samples increase in going f
28. the PCA button under the menu bar 4 In the options dialog make sure Remove samples marked as unused is unchecked and click OK The PC s will be recalculated and the display regenerated Note that the excluded sample is still present in the display but is drawn using the excluded symbol and that this is reflected in the plot legend If the legend is not displayed right click in the scores plot and select Display lt Show legend If Remove samples marked as unused had been checked the sample would not have been included in the display If you save the data at this point the resulting file will still contain the excluded sample but it will be marked as unused 4 3 3 Selecting and displaying the behavior of variables As noted previously the variables with large positive PC1 loadings are the ones most likely to be responsible for separating the two groups since one group has large positive PC1 scores 1 Draw a selection rectangle around the points representing the variables with the largest PC1 and PC2 loadings 1296 69 and 1297 69 c x amr 8 3 Loadings for PCT 71 1 2 versus PCZ r 2 x Pareto Default a Isotope Monolsotopic 0 14 4 1233 53 2 Click the Plot profile button l This will generate a new pane in the same window showing the intensity profiles of the selected variables Bg A1 MS 1 2 how ATO 1 t2d 54 5 1 6 MS 1 Response AG MS_ Fad C4 MS 12d
29. the samples are arranged in index or acquisition order In the Sort Order pull down list select Sample Index E 5 Ed 8 3 353 37 20 340 w Sample Index ss Group Order Rat3 8 16h vinpa A Rat3 15 24h blk A Hat3 8 TBbh blk 4 Hat3 D Bh Blk A Rats Bh vinpo 4 Response Hat3 15 24h blk A Hat3 D Bh blk 15 24h blk Fat 8 10 blk amp D Bh blk ample by index 5 The graph illustrates that the samples were run in order i e rat 1 followed by rat 2 and rat 3 and that this variable appears to be a contaminant that occurs later in the analyses and is only present in rat 3 Hide the two tables and the scores plot by clicking in the Hide pane button 1 in each of these panes This results in a display that consists of the loadings plot and the profile graph making it easier to select variables Search for variables that have similar behavior by clicking on symbols in the direction of family 6 but closer to the origin To make this easier you may need to zoom the graph either by dragging in the axes or by selecting a rectangle in the graph right clicking and selecting Zoom Selection As the variables are encountered draw a small rectangle around them right click in the rectangle and select Don t use Selected Peaks for Subsequent PCA The symbols will be replaced by the symbol for excluded points by default an open blue circle The finished display will resemble
30. 0 MS 1 r2d M5 700 6017 Spectrum from L1 M5 1 r2d M5 700 BOT 7 Spectrum from L2 5 1 r2d M5 700 BOT 7 972 57 1 8000 BD S000 37357 1 4000 Intensity 3000 us 374 58 1 1 1008 370 81 1 gzon 375 55 1 n i d i 370 48 1 371 40 1 jt 0 2 Sb e ie gt un i dini cu um H1 qe 4 M ass Lharge Da 375 55 1 ae The graph is zoomed so that the selected peak is centered in the display In this case it is clear that the reported difference is real i e that the peak at 972 57 is intense in samples from group A and much less intense in those from group C For this figure the Use Group Colors for Traces option from the Display submenu of the graph s context menu was selected so that the group color is used for each trace rather than a different color for each this makes it easier to tell at a glance to which group a given spectrum belongs You can click on the magnifying glass icon Q to make the pane containing the spectra fill the display windows When you have finished examining the data click the icon again to return to the normal display 3 10 Summary In this section you have e Imported a set of MALDI TOF spectra and reviewed the sample and variable data e Reviewed the samples assigned them to groups and created a symbol for each group e Performed a t test to determine how well each variable distinguishes the two groups
31. 06 peaks LL M5 Data wiff sample UL 7 peaks LC MS Data wiff sample 000 peaks LC MS Data wiff sample O09 peaks LU MS Date watt sample 01 0 peaks LC MS Data wiff sample 011 peaks LC MS Data wiff sample 012 peaks gu E E E E E 3 The dialog box that appears resembles that seen in section 5 1 but has some additional parameters to control the way the data is filtered Fill in the fields as shown below and click OK MarkerView 1 2 1 Software User Manual 50 Revision February 2010 Alignment amp Filtering Alignment Retention time tolerance 1 B min cw Mass tolerance 25 ppm Filtering Intensity threshold 5 Retention time filtering Remove peaks in gt samples Use exclusion list 5 Maximum number of peaks 8000 Area Reporting Use area integrated fram raw data not from original peak finding Internal Standards Perform retention time correction Perform sample normalization When the import process is complete the sample table as in section 5 1 will appear Since importing peak lists is much faster than importing from the original data files you may want to experiment with the different parameters and observe the effect on the PCA displays Particularly important are the alignment parameters since these determine if peaks that are close in m z and or retention time will be combined or not The intensity and minimum retention time parameters
32. 1 t2d Sample by index This kind of change is quite common and can be caused by a number of gradual changes in the instrument or the samples In this case the data were acquired in the order they are displayed i e all group A samples were analyzed before group C which can cause this kind of variation to appear as real differences between the two groups To avoid this the samples should be acquired in a random order so that members of both groups will be equally affected by experimental variation This simple example illustrates an important point these techniques will find differences and can be very sensitive to small changes between groups but in order to determine real biological changes of interest the experimental system should be as closely controlled as possible 4 5 Summary In this section you have e Opened a saved data set e Performed a Principal Components Analysis PCA on the data MarkerView 1 2 1 Software User Manual 25 Revision February 2010 Understood and interacted with the display Excluded outliers or abnormal samples from a graph Displayed the profiles of variables for all samples Showed the raw data corresponding to the variables Examined the data to reveal that there are some experimental variations These are the basic operations for using PCA and will be used frequently The next section will apply these techniques to LCMS data and show how variables can be excluded from the calculations wh
33. 1041 28 flsotopel ps 819 12 Ew o A 1458 26 Manoisotopic 1210 23 E p T eae 06 1450 29 06 22 5 a Log Fold In this particular case 708 19 Da the variable clearly represents only noise Since the peak was not detected for the C samples the fold change was reported as infinite The plots and the table are linked so the profile plot will update as you select different variables in the lower display By zooming the vertical axis of the p value vs log fold change display you can quickly select the variables that provide the greatest discrimination between the two groups Delete the profile plot and zoom the p value axis Select all points with a p value less than 1e 4 as shown below right click in the graph and select Show Points In Table MarkerView 1 2 1 Software User Manual 66 Revision February 2010 Log Fold Change versus p value for A to C Saved 8 8 8 sen ais Indes Peak Mame miz Ret Time Group I value p value Mean 1 Mean 2 705 38 705 3789 N A Moenaeisotepic 5 30 5 Bhb4e h 8 54882 1 206283 a ja ss mese wa a or assis aser estas m fa mes e a stoner Ie 45 ums sme ze s s es empa Rie ner saser sare Hs fs os wma I sms zue sre qz fa wa m wa aes e 42r o 225ez 546 s fa ws rare wa
34. 2 1 Software User Manual 38 Revision February 2010 Principal Component Variable Grouping Eg Common Peak Selection C Use active PCs f Mumber of PCs 3 C Use PCs explaining 30 gt of variation Angle delta 40 degrees Min distance from arigin 0 02 Iw Assign Groups Automatically Only start a new group if PC with max loading is used Min distance fram origin to start new group 0 05 Group correlated and anti correlated together Skip groups with lt 0 peak s Sort by Magnitude The loadings plot should appear as shown below The variables have been automatically assigned to one of six groups in addition to the Default group for certain very small variables These groups roughly correspond to the numbered groups discussed in step 4 of the previous section section 5 3 One reason that the grouping is not identical to the visually identified groups of the previous section is because the automatic groups were assigned using information from the first three principal components as selected in the figure above whereas the visual grouping was based on only the two visible components This is an important point since it allows a two dimensional display to be colored in such a way that additional variation not otherwise visible can be seen For a detailed discussion of this tool see the MarkerView Reference Manual The concepts underlying the grouping itself are discussed in the following paper Dimensiona
35. 3 az 2 403 aino faa HZusoss6 suns 345 8 reormoscion me 106 07108 m fa emm sao pez i3 2 ha na p f OA HM ar m PC2 Loading Note that the list contains a Current column which is checked for some variables and not for others Each PCA plot maintains a list of the variables that were excluded when the display was generated these do not have a check mark in the Current column and a list of the peaks that are excluded in the display but were in use when the display was generated these have a check mark In the figure above the first 20 variables were excluded before the display was generated i e steps 12 and 13 in section 5 3 above the rest correspond to the ones selected after these plots were generated This is also reflected in the status bar at the bottom of the main window 41 Peaks 171 Currently Escluded Peaks Interest List Peaks 20 Previously Escluded Peaks which in this case indicates that 20 variables were previously excluded and 171 have been selected and excluded The Excluded Peaks list behaves as a normal table You may sort on any column by clicking on the column heading and then of one of the two sort buttons You may select one or more columns by dragging in the column headings and these can then be copied to the clipboard by typing ctrl C or selecting Copy from the Edit menu and pasted into another program such as
36. 55 5 Q EXC 700 15 AD MS 1 t2d C1 MS I 3 MS ltd ened A10 MS 1 12d i A MS C3MS 12d c7 Response A2 M 51 td 85 5 1 t2d AB M5 1 t2d C1 M5 1 t2d 4 M5 1 t2d 7 M5 1t 2d C10 MS Sample bv index MarkerView 1 2 1 Software User Manual 9 Revision February 2010 3 3 Reviewing the samples and assigning groups 1 Select Show Samples Table from the View menu The Sample table will be displayed below the Peaks table Index Peak Mame m z Het Time Use A M5 1 1 700 15 T001481 NAA 5 1 eo P ma meaa 3 p on osp 70 0 DE Tore pnm B 701 4851 Naa 1 331e2 Sample Mame Sample ID Group Use AT_MS_1 ted AIO MS 1 t2d r The table contains a row for each sample with columns indicating if the sample is to be used in subsequent processing Use the scale factor for this sample the associated group and other optional information The Sample ID is obtained from the data file as is the Acquisition Time if the data are being imported from a wiff file The Scale Factor can be adjusted to allow for overall differences in the amount of sample used and the RT Correction is used to adjust the retention time in LCMS analyses The Group information is used in supervised analyses and to select plotting symbols for the samples so that differences are more apparent 2 Select all of the rows for samples with names starting A by dragging i
37. 69 24 Response 300 38 1045 54 1083 52 1299 64 1300 63 yea 73843 86241 90648 97256 1099 55 1571 68 1757 87 2034 10 2466 23 05341 anis y 110057 1759 88 178875 2455 23 Je 760 93 82333 31014 99950 1101139 123411 1361 70 151782 1691 63 31830131 2133 33 Peak If you click on the Link to Table button E amp in the graph header the graph will update when different samples are selected You can zoom or scroll the plot by dragging in the axes as in the Analyst Software Click on the trash can button in the graph header to delete the graph Select a row by clicking in the row header to the left of the row number and click the Plot Row icon uf The graph shows how the value of the selected variable changes for every sample in the table Select the Link to Table button in the graph pane and then click in the table to make it active the active pane is indicated by an orange border Select a new row in the table and the graph will update to show the behavior of the selected variable for all samples When the table is active you may also use the arrow keys to change rows and quickly review the data When you have finished reviewing the data click the trash can icon to delete the graph 700 15 Sel Index Peak Mame m z Het Time Use A M amp 1 r2d 7 0 15 TODO T1481 M amp 6 02061 ma mosea mors p ma mm a r01 7969 N 0 00e0 lt 8
38. 8 280 17127 238 4 143 0 24 5 E7 p ty 2521 128 183 4 ET If the variables are normally distributed we expect the mean and the median to be identical i e the plot should be a straight line with a slope of one While there are many variables that meet this condition there are also several that have a lower median than expected zero in some cases This arises when the data is not normally distributed for example there may be two groups with the variable absent zero in one group in this case depending on the number of samples in each group the median may be zero while the mean is not In any case these are likely to be interesting variables MarkerView 1 2 1 Software User Manual 62 Revision February 2010 11 Make a selection rectangle around the variable 266 1 12 8 right click and select Show Selected Points In Table and then click the Plot Profile tool in the table s toolbar In the profile plot click the Sort Order tool to get the following display 2 266 1 12 8 209 LEMS Saved Pe ju h 3 5 CQ Ed EJ Index Peak Mame m z Het Time Group Charge Mana Mass Mass Defect 209 266 1 12 8 209 266 1190 Monoisotapic 210 210 2661 153 210 2661200 1532 jai 29 asasen 2661352 1377 jaz faz sz861 714 012 2661482 1142 317 13 A PRS TATA 707171 PRY 1187 Fi 7 55 E 8 3 3 206 1 12 8 213 Bh vinpa 3 Hat3 8 1Bbh vinpo 5 Hatz 0 0 winpa amp Rat 8 1Eh vinpo
39. 9 Revision February 2010 oadings for PC1 71 6 versus PC 7 4 Pareto How Indes Sample Mame Sample ID Het Time l MS 1 t2d T N A 1 1 z ese 24 kiC do 5 HR C Fmt 3 Scores for PCT 71 5 4 versus PC 7 4 x Pareto Loadings for PUT 71 5 X versus PC 7 4 x Pareto A10 M5 1 t2d 1296 69 Default 4 Isotope 3l 4 1297 69 Monoisotopic AB_MS_1 t2d 1299 64 AB MS 1 2d e 100352 15055 one 47 r 2467230 t 1046 55 1298 59 E sao 5 M5 6 06 MS 1 Ws OR i 8 d 2093 10 2 M5 104 A2 M5 1 4 P 353 2096 11 AS MS 1 02 PE i 3 M5 A3 M5 1 t2d 0 0 0 1 1 PCT Score PC1 Loading 05 MS C10 8 142d e Ci MS 1 Al MS 1 t2d PC Score PC Loading 4 2 Understanding the display As mentioned above PCA determines linear combinations PC s of the original variables that explain the variance in the data i e PC Pix P2X2 P3X3 where the p s are called the loadings and represent the importance of the variables x to the PC the larger the loading the more important the variable You can think of this as follows if there are n variables originally then every sample corresponds to a point in the n dimensional space defined by the variables PCA is equivalent to rotating the axes so that one PC1 lies along the line of maximum variance T
40. 91 1 11 3 f 31 1 11 3 10 Rats 15 24h vinpa 4 Fat amp 1Eh blk A Rat 8 T5h blk A Rat3 16 24h blk A Hat 8 16 vinpo 4 Rati 15 24h vinpa Rat 8 15h blk A Response Par 0 8h blk Fat 16 24h_bik amp oo ee Rat2_0 Sh_blk A Rat3 D Bh vinpa A Fh blk 4 D Bh vinpa 4 RHat3 8 16h vinpo Rat Bh blk amp 8 bh blk Rat3 1b5 24h blk 4 Sample by group Note that the variables in this direction show the opposite behavior to those in direction 4 i e they are lowest in the 0 8 samples In this case there may also be some difference between the pre and post does samples 8 Click on the variable furthest from the origin in direction 6 353 3 20 7 MarkerView 1 2 1 Software User Manual 35 Revision February 2010 mo 353 3 20 7 340 Rat 16 26 vinpo Rat3 8 16h blk A Rat3 15 24h bk A Hat3 8 1 Eh vinpo 4 Response Rata_0 8h_blk 5 Hat3 0 0 vinpo amp blk 3 Hat2 B8 15h blk 5 Rat3 15 24h blk amp 0 80 0 Bh vinpa amp Aata B8 16h vinpo Sample by group At first sight this variable appears to be present only in the rat 3 samples and two of the rat 3 blanks have the largest negative PC1 scores but there may be other explanations for this behavior for example a systematic variation To check this it is useful to switch the display so that
41. AB SCIEX MarkerView Software Yersion 1 2 1 User Manual MarkerView 1 2 1 Software User Manual 1 Revision February 2010 SO 9001 REGISTERED COMPANY Revision February 2010 This document is provided to customers who have purchased AB SCIEX equipment to use in the operation of such AB SCIEX equipment This document is copyright protected and any reproduction of this document or any part of this document is strictly prohibited except as AB SCIEX may authorize in writing Equipment that may be described in this document is protected under one or more patents filed in the United States Canada and other countries Additional patents are pending Software that may be described in this document is furnished under a license agreement It is against the law to copy modify or distribute the software on any medium except as specifically allowed in the license agreement Furthermore the license agreement may prohibit the software from being disassembled reverse engineered or decompiled for any purpose Portions of this document may make reference to other manufacturers and or their products which may contain parts whose names are registered as trademarks and or function as trademarks of their respective owners Any such usage is intended only to designate those manufacturers products as supplied by AB SCIEX for incorporation into its equipment and does not imply any right and or license to use or permit oth
42. Excel 9 Perform another PCA analysis and display the excluded peak list verify that the new list has no checkmarks indicating that all of the listed variables were excluded before generating the display The analysis display will resemble the one shown below however depending on exactly which variables you have excluded the display map flip about the PC2 axis MarkerView 1 2 1 Software User Manual 44 Revision February 2010 oadings for PC1 55 4 versus PC2 14 2 Pareto LEMS Saved Sample Mame Sample ID Index Peak Mame m z Het Time O 8h_vinpo 4 81 1 13 3 1 61 0637 13 32 ALAS 1 381 6 5 Fatt f Ah hlk Smig Scores for PC1 55 4 X versus PC2 14 2 x Pareto Loadings for PCT 55 4 x versus PC2 14 2 Pareto Rat 15 24h blk 358 2 12 5 354 Default 1255959 Isotope 149 0 6 3 49 1B i 149 0 13 0 71 0 Monoisotopic B7 5 4 024 1 d 6 2 381 225 1 13 1 141 He ge 276 1 12 5 224 9311 12912 Mop 413 17117 406 85 0 12 5 5 3n Ratl 1824h vinpo A 990 71 57 4 61 1413301 1 Fari B 15h vitipo A Fat 8 16h 56 hat O 8h bKEA 9 ha 08h bki A i m a 388 2 11 3 387 eccesso 0 P p 3 013 10 e Rag 818 6 8 591 2 12 3 426 Rat3 86h blk A n ME cs 0 1 12 3 278 Rat3_16 24h_blk A 3021 24 288 0 0 0 2 PCT Score
43. OK The files will be processed individually and a peak list file generated for each MarkerView 1 2 1 Software User Manual 49 Revision February 2010 Note the folder named Peaks already contains the peak lists for these samples so you can skip the last step if you wish by clicking Cancel instead of OK 6 1 2 Importing peak list files 1 From the File menu select LC MS Peak Lists peaks TE MarkerView mi Edit View Analyze Window Help Create LC MS Peak Lists From wifF i Open Ctrl O LCIMS Data from wiff Recent Files k MCA Spectra From wifF Save As Ctri 5 MALCI Spectra From T2D Export Peaks Table 4x00 LC MALDI Peak Lists From text MRM Chromatograms From wifF P Set age Setup Exported Analyst Results Table Print Preview Pane Text Spectra Generic Text File Print Preview Window Print Pane Ctrl F Print Window 2 Locate the Peaks folder in the MarkerView Example Data LCMS data or the folder you created in section 6 1 1 and drag it to the right side of the display Click OK Select Peak Lists Select Falder C Program FilesV4B Sciex Markerview 1 0 5 ample Data Available Selected Sample Data Sample Data 9 LEMS Data 9 LEMS Data EJ Peaks 2 5 Peaks 3 MALDI Spectra LC M5 Data witt sample 002 peaks LC MS Data wiff sample O03 peaks LL M5 Data wiff sample 004 peaks LC M5 Data wiff sample 005 peaks LC MS Data wiff sample O
44. Raz 8 15h blk g Raz 15 24h vinpo 4 h Ratz 15 24h blk A Bg Rat3 D 8h vinpo A g Rat3 D 8h blk A 3 Setthe Minimum retention time to 12 min click to check the Maximum retention time check box and enter 14 min Set the other parameters as shown below these are the same settings as used in section 5 1 and click OK Peak Finding Options M Data to Process Experiment Period 1 Experiment 1 Minimum retention time miri iv Mazimum retention time 400 j miri r Enhance Peak Finding Subtraction offset scans Minimum spectral peak width 5 ppm Subtraction mult Factor Minimum AT peak width 20 scans Noise threshold 5 M More IW Assign Charge States 4 The next dialog box allows you to set the alignment and filtering parameters While the purpose of alignment is mainly to ensure that peaks in separate files with similar m z and retention time values are assigned to the same variable it is also applied to the peaks within one sample Set the Retention time tolerance to 1 min and the Mass tolerance to 25 ppm Click OK MarkerView 1 2 1 Software User Manual 52 Revision February 2010 Alignment amp Filtering M Alignment Retention time tolerance 1 OO Mass talerance 25 ppm Filtering Remove peaks in gt samples Use exclusion list el I asimum number of peaks s000 Area Reporting Use area integrated fram raw data not from original peak fi
45. S 333 5 Mass Charge Da 399 0 338 5 12 2 12 4 12 6 12 8 13 0 13 2 13 4 13 6 Time min 11 Right click in the contour plot and select Show Peak regions for All Peaks Ellipses will be drawn around the areas where peaks were located and the extent will indicate the time duration and m z width of the peaks found The same command is available when more than one peak has been imported but in this case the ellipses will indicate the combined extent of the peaks in all samples T em CS ES Hatl vinpo amp fram LEMS Data witt sample 2 Poste utra b UN pe tii a cess rea itl as Vat p er sd na data 401 0 400 5 Raced Cor vli RB adil n Nea s RETIRO EET CEN 3 8 mai 400 0 UU RE HUP TM m ca a En m Ta on m 333 5 333 0 uis 398 5 Log Scale T2 d 12 4 12 6 12 8 13 0 13 2 13 4 13 6 Time min The display shows that for m z 399 2 the peaks at 12 35 and 12 6 min were found as a single peak the ellipse covers both and the peak at 13 1 min was also found For m z 400 2 only the peak at 13 1 min was found and there appear to be several other small peaks in the area that were not found 12 Right click in the contour and select Show Tooltips As you move the cursor in the contour plot a tool tip will appear indicating the m z retention time and intensity z of the point under the cursor In this case the intensity of the peaks in this area do
46. amp Hespanse 0 Hat Bh vinpa 4 Aaa 8 16h_vinpo 4 0 0 blk 4 Hat B Eh blk A amp Rata 15 24h blk A Sample by group 5 HR ES EJ Mean versus Median 81 1 11 3 10 340 1 12 3 326 105 071 ine 22 323 2 13 0 301 302 1424 gil 387 2711 3 384 266 1 12 8 209 300 This is clearly a drug metabolite and the number of samples in which the variable is zero is the Same as the number where it is non zero and the overall number of samples is even Hence the median will be the average of zero and the smallest non zero value while the mean will be the average of all samples in this case the latter is higher If the variable was zero in more samples for example because it is metabolized quickly then the median would be zero 12 Select one of the points with a large mean but close to the median 0 axis Since the table and both plots are linked the variable will be selected in the table and the profile plot generated Click the Sort Order button to get the display shown below In this case the variable corresponds to some contamination that appears late in the run and is therefore most obvious in Sample Index order Because the number of zero values is greater than the number of non zero values the median is zero while the mean is still positive Zooming the display to better view the points that are close to the median 0 axis and clicking on variables quickly reveals variables that belong in t
47. amples ta Keep Remove samples marked as not used PCA Preprocessing determines how the data will be treated prior to the actual PCA analysis PCA determines the variance of the data and is most affected by the largest data values hence it is normal to scale the data so that variables have equal importance regardless of the magnitude The most common method is known as Autoscaling and is available from the Scaling menu Experience has shown however that for mass spectrometry data Pareto scaling is a good first choice Pareto scaling reduces but does not completely eliminate the significance of the intensity which is appropriate for MS because larger peaks are generally more reliable and all variables are equivalent Different scaling methods can reveal different features of the data and it is worth experimenting with these settings to observe this behavior 2 Select None for the Weighting and Pareto for the Scaling as shown Make sure that the Perform PCA DA option is unchecked PCA determines combinations of the original variables that explain the variance in the data The first principal component PC1 explains the greatest amount of variance PC2 explains the next largest amount and so on The program will stop calculating PC s when the amount of variance explained is less than 0 5 of the total variance 3 Click OK After the PC s are calculated the following will be displayed MarkerView 1 2 1 Software User Manual 1
48. are User Manual 34 Revision February 2010 7 Ed C ES 105 0 108 22 D 8h blk A Fats 0 0 vinpa 4 amp 180 1 10 8 100 D Bh blk A Rat3 D Bh bik 5 0 80 vinpa A EJ vinpo A 2 0160 vinpo A uL ENS 0 00 2 353 1 10 8 345 Response Bat vihpo 4 1 12 A mci Messe PE Rat 0 8 4 Hatl 15 24h blk 4 Fat B8 1Eh blk A Hat3 0 8 amp Hat3 1B5 24h blk 4 Sample by index 6 In the toolbar of the new graph click on the downwards pointing arrow adjacent to the Sort Order button and make sure Group Order is selected The data will be drawn in group order where the groups are sorted alphanumerically i e in this case the order is 1 2 3 blank1 blank2 blank3 Xd um 105 0 108 22 v Sample Index jati 8 180 1 10 8 100 TENES Q 358 1 10 8 345 Hesponse The profile graph shows that the selected ions do behave as expected i e they are more intense in the 0 8 hour samples than the 8 16 and 16 24 and comparable in the pre and post dose samples Click on other variables in the direction of family 4 to update the profile display and note that they all have similar behavior although the peaks get smaller and the noise higher closer to the origin 7 Click on a variable that is furthest from the origin in the direction of arrow 5 e g the variable
49. bles are removed before performing PCA but here their presence helps to confirm that the observed behavior is real and not random It also provides a way to determine peaks that are related to the same compound since these will be correlated 4 3 Interacting with the display This section describes some of the features of the displays and ways in which you can interact with them The displays contain many powerful features and it is valuable to experiment with them MarkerView 1 2 1 Software User Manual 21 Revision February 2010 4 3 1 Displaying other PC s 1 Inthe scores table 1 select the PC1 column by clicking its title and then select PC3 by holding the Control key down while clicking its heading scroll the table sideways if necessary TE Scores Pareto Index Peak Mame m z Het Time ve 700 15 7001481 N A J ANAA 1 88 8 ke Loadings for PCT 71 6 X versus PCS 2 4 X Pareto Default 736 431 44 M5 aC a Isotope Scores for PC 71 5 X versus PCS 2 4 l Pareto C1_M5_1 t2d L2 5 Monaisotapic i 737 44 bog BEM bud A5 MS 142d doen C10 MS 205 904 47 AB M5 972 56 905 47 1 A3 MS 1 t2d PCS Score PCS Loading amp C3 MS ETT ATO MS 1 t2 5 06 MS 1d A MS 1 t2d CE MS 1 t2d X A4 MS 142d MSL 2093 10 LT MS vas ABMS 112d 010 PCT Score PCI Denm The display w
50. ctor RT Correction Ratl_0 8h_vinpo 4 LCMS Data wiff sample 1 000e0 None 2 Please erter the desied group name uo 0 Ne 7 E 4 ir 3 Lp 4 LT persa 19949 Ne s s Baie2We A lMSDaawifange sched 199 Ne So Tra 162 CNN e m Nae Rat2 Oh vinpo LEMS Data wiff sample 2 otop 1 000e0 None fe e Raz M po 0 0 5 Ge E Lime Ne Hu rah BRA UMS Data ante Brkt Hi Ra 15 26h ee k LHS Baa eagle T T Emme ria e a E027 RN S NR RR 13 Rat3 D 8h vinp LCMS sample 1 2004 11 10 8 12 PM 1 000e0 None E DE QN RN Hs s PeeiGsres A E 199966 Ne He i Resim ERA tMSDaaifemei 196 Nm v BaiezWoe A lMSDaawiiempel e rumes Ne i 1s Rendez LOS E 100060 Nme 13 Repeat the process assigning the groups as follows Rat1_8 16h_vinpo A Rat2 8 16h vinpo A Rat3 8 16h vinpo A Rat1_16 24h_vinpo A Rat2_16 24h_vinpo A MarkerView 1 2 1 Software User Manual
51. ctra Generic Text File Exil 2 Inthe Select Samples dialog box that appears click the Select Folder button and locate the folder containing the example MALDI spectra This folder is installed to the AB Sciex MarkerView Sample Data subfolder of the Program Files folder Select Samples Select Folder C Program Files V4B Sciex Markerview 1 0 S ample Data Available Selected 5 03 Sample Data 9 LEMS Data MALDI Spectra 3 Select the folder MALDI Spectra and click the button marked gt alternatively you can drag the folder to the right side of the display marked Selected MarkerView 1 2 1 Software User Manual 6 Revision February 2010 Select Samples Select Folder C Program FilegAB 5cieg sM arkerVview 1 0 S ample Data Available Selected Sample Data MALDI Spectra El 5 ATD MS 1 t2d A MS 1 t2d A2 MS_1 t2d A3 MS 1 t2d 54 MS 1 tzd A5 MS 1 tzd Ab MS 1 t2d Af MS 1 t2d AB MS 1 t2d AS MS 1 t2d E gg ga uw L10 MS 1 rzd OK Cancel 4 Click OK to import the files on the right side of the display In the Process Spectra Options dialog select Find spectral peaks in profile spectra and enter a Mass tolerance of 200 ppm set Minimum required response to 100 Maximum number of peaks to 5000 and ensure Use exclusion list is unchecked Click OK a dialog box will indicate the progress of the importing operation Process Spectra Options Processing
52. d so this sample needs to be removed 3 In the right hand display select the first sample and click the lt button to move it back to the left hand side so it will not be imported You can verify that the sample has been removed by expanding the LCMS Data folder and data file in the left hand pane by clicking on the signs Data courtesy of Dr Gerard Hopfgartner University of Geneva Vinpocetin is known as a memory enhancer a treatment for Alzheimer s disease a treatment for stroke it improves circulation especially to the brain and it is a powerful antioxidant MarkerView 1 2 1 Software User Manual 27 Revision February 2010 Select Samples Project Browse C Program Filez amp B 5 cies M arkerview 1 0 5 ample D ata Available Selected Sample Data Sample Data LEMS Data LEMS Data E Peaks 5 LEMS Data wiff E LEMS Data wiff _0 8h_vinpo A MALDI Spectra Rati D Bh vinpa A Rati 0 8h_blk A FRatl_8 16h_vinpo 5 Hat 8 15h blk amp 16 24h_vinpo amp Rat 15 24h blk Bh vinpa amp Rat D Bh blk amp B 15h vinpa 5 Hatz 8 1bh bhlk 5 Fate 1b 24h winpo 5 Hatz 15 24h blk 3 Hat3 O Sh_vinpo 5 4 Click OK to begin the import process Importing LCMS data occurs in two separate steps the first step locates the peaks in the data and the second step performs the alignment and normalizat
53. data by selecting Normalize LC MS Using Internal Standards from the Normalization sub menu of the Analyze menu If you have not used internal standards you can still normalize the data but this should be done carefully since there is no real way to ensure that the selected peak s should indeed be constant for all samples The following describes the process for the vinpocetin data used in earlier sections 1 Open the LCMS data file you saved in section 5 2 step 8 Here are some tips for picking peaks to use to normalize in this way The peak should appear in every sample and preferably be a single peak i e have no close isomers that may be picked incorrectly The intensity should not be very small noise or very large possibly saturated There should be no or little dependence on the group There should be no systematic variation click the Sort Order button and select Sample Index to look for this Examination of the data shows that the peak at m z 384 1 and 10 5 min appears in all samples and although it may have some group dependence this is not large and we will assume it is suitable 2 Select the row containing this peak and plot its profile using the Plot row button Verify that there is no systematic variation and the group dependence is relatively low 3 Make sure the data table is the active pane and the 384 1 10 5 row is selected and select Normalize Using Selected Peaks from the Normalization submenu of the Ana
54. e Reviewed the behavior of certain variables for all samples e Used the raw data to confirm a difference between groups e Detected the presence of a suspicious sample and deactivated it from further calculations These steps are the basis of all data processing in the application and many of the operations are common regardless of the data and the type of analysis The next section shows how the same data can be reviewed using unsupervised techniques to confirm or identify groups detect outliers etc You may also want to look at section 6 4 Selecting discriminating t test variables to see additional ways of determining variables that best distinguish the groups MarkerView 1 2 1 Software User Manual 18 Revision February 2010 4 Reviewing the data with PCA In this section you will learn how to review the data using an unsupervised technique Principal Components Analysis PCA Close any open windows and then open the data table you saved in section 3 4 by selecting File lt Open and locating the saved data file The data table will be displayed 4 1 Performing Principal Components Analysis 1 Select Perform PCA from the Analyze menu HIE MarkerView File Edit View ett aa Window Help Perform PEA CEri A Compare Groups with E Test Ckri T Normalization The options dialog box will appear PCA Options FPCA Preprocessing Weighting None Scaling Fareto Perform PCA D supervised S
55. e e a 3 238 12 81182 b 383 2 15 3 375 SLI 104 gt 5 3381 12 1 321 Cz pss es T 381 110 8 365 iss 100 h 10 8 224 0 00 0 05 0 10 0 15 0 20 PC1 Loading 266 1 12 6 203 rp 238 PA cn 5 gm m a tg DJ C LL As explained in section 4 2 Pareto scaling causes correlated variables to lie on straight lines that pass through the origin Examination of the loadings plot indicates the presence of several families of correlated variables such as those shown above The families marked 1 and 2 have the highest positive PC1 loadings and will contribute most to the separation of the post and pre dose samples although families 3 and 4 may also have an affect The variables indicated by the line marked 7 may also contribute to this difference but in the opposite sense if 1 and 2 correspond to variables present in the post dose but not the pre dose variables in family 7 will be predominantly in the pre dose samples The variables in family 4 seem most likely to be in the 0 8 hour samples pre and post dose since these had the largest negative PC2 loadings whereas 5 and 6 will be more prominent in the 8 16 and 16 24 hour samples We will start by exploring the diurnal variation 5 Click the magnifying glass to return to the multi pane display select the variables that are furthest away in the direction of arrow 4 and click the Plot Profile button MarkerView 1 2 1 Softw
56. e negative values relative to a higher integer e g 300 8 would have a defect of 0 2 Relative to the lower integer In this case the defects are always positive i e 300 8 has a defect of 0 8 To change between the two modes right click in the Peak Info table and click Signed Mass Defect 9 The Peak Info table also allows columns to be plotted either individually or one may be plotted against another This can help visualize characteristics of the data or select particular variables for example in choosing variables to use for normalization it might be appropriate to select variables with relatively high values mean or median that are relatively constant low standard deviation plotting sigma against mean can help select such variables 10 In the peak Info table select the Mean column and drag to include the Median column click the two way plot icon to get the following display the sample table has been hidden for clarity IE Peak Info LEMS Saved e de 34 EI 8 X 3 ES Index Peak Mame m z Het Time Group Use Charge Mona Mass Mass Defect Mean Median 81 1 13 3 1 81 0637 72 HAA N A 0 064 p p lemsa ew ms a m wa os Re 11 11 7 kl 7 l Bl A n re PE Sa 3 1499 Tem C Ed EJ Mean versus Median 81 1 11 3 10 d 300 323 2 13 0 301 105 0 10 8 22 134 1711 3 111 387 2 11 3 304 266 1 1 2 8 203 143 0 4 1 70 302 1 12 4 25
57. e0 03 1 13 8 20 1020507 13 81 00 o 000e0 o 000e0 Use Index Peak Name 81 1 13 3 1 0060 sue me emra soe me Fave e ze resonese 66 zs a C E m z 81 0537 Het Time 13 32 Group bad 85 0 12 4 E 05 0200 2 83 0 11 5 7 99 0346 91 1713 2 5 81 0507 10 81 1 11 3 10 91 0511 11 29 i 9D E 11 3 8 90 6161 4 143 i 91 1713 9 11 91 0534 13 99 rna isse 500 sume us 10009 suse vere is 500500 sum us C DJ J cn m 95 1714 3 17 95 0837 97 1 11 3 18 97 0611 11 26 oo ral 4 cn Co oop 2 co r3 c ca Ph Ll Pay r3 104 0 11 4 21 104 0464 11 44 Monoisotopic gt gt 5 2 Assigning groups and symbols It is convenient to assign groups and symbols so that the pre and post dose samples can be distinguished as well as the different time points We will assign the groups and symbols according to the following table Time point hr Post dose Sample symbol Pre dose Blank symbol group group Closed blue circle blank1 Open blue square 1 Closed red circle blank2 Open red square Closed green circle blank3 Open green square So that the time points are distinguished by color and the pre and post dose b
58. ee section 5 3 Performing PCA and interpreting the results When this box is checked the software first performs PCA as normal using the weighting and scaling parameters specified which reduces the dimensionality of the data by generating a few PC s that are MarkerView 1 2 1 Software User Manual 46 Revision February 2010 combinations of the original variables The PC s are then combined with the group information to find combinations that maximize the variance between groups while minimizing the variance within groups This can often dramatically enhance the appearance of the separation as shown by the scores plot the results are interpreted as before 1 Close all open windows and open the data table that was saved in section 5 2 2 Perform PCA with no weighting and Pareto scaling but click on the Perform PCA DA supervised checkbox to select it The result is shown below oadings for D1 32 0 versus D2 17 0 Pareto DA LCMS Saved PE Sample Mame Sample ID Peak Mame m z Ret Tiri Fatl_O Sh_vinpo 5 LEMS Data watt p 81 1 13 3 1 61 0637 1332 m amp CQ ES oc BR Des 3 Es Scores for D1 32 0 X versus D2 17 0 4 Pareto Di Loadings For D1 32 0 versus D2 17 0 Pareto DA IRat2 8 16h vinpn A 91 1711 3 010 Default Isotope 134 1 1 3 111 Monaisotapic 8 1 bh vinpoa 3 ij ee 3 0 396 a 39 2 11 3 384 15 24h v
59. en they are not of interest MarkerView 1 2 1 Software User Manual 26 Revision February 2010 5 Unsupervised processing of LCMS data In section 4 you learned how to process data using PCA this section applies this technique to more complex samples resulting from the LCMS analyses of a time point study The data set was obtained by analyzing the urine from three rats at three different time points 0 8 8 16 and 16 24 hour before and after administration of vinpocetin at 10 mg kg Samples were analyzed by LCMS on a QStar XL 5 1 Importing data 1 Form the File gt Import menu select LC MS Data from wiff HE MarkerView mi Edit View Analyze Window Help Create LC MS Peak Lists From wifF LCIMS Peak Lists peaks Open LCIMS Data From wifF Recent Files k MCA Spectra From wifF Save As CEri4 5 MALDI Spectra From Export Peaks Table k 4 00 LC MALDI Peak Lists From text MRM Chromatograms From weifF Page Setup Print Preview Pane Exported Analyst Results Table Text Spectra Generic Text File Print Preview Window Prink Pane Ctrl F Print Window 2 Inthe Select Samples dialog navigate to the example data folder and drag the folder LCMS Data to the Selected side of the dialog see section 3 1 for additional details Note that the first sample Rat1 0 8 Vinpo_A is included in the file list twice After the first injection of this sample the chromatographic conditions were change
60. ent more easily with the alignment and normalization parameters In addition both steps have separate minimum intensity parameters so you can use a very low threshold to find the peaks initially and later reject small peaks that may be due to noise 6 1 1 Generating peak list files 1 Select Create LC MS Peaks Lists from wiff from the File menu MarkerView mim Edi View Analyze Window Help Create LCIMS Peak Lists From wifF Import k Open Chro Recent Files k Save As Ctrl 5 Export Peaks Table k Page Setup A dialog box will explain the purpose of the command Click OK to dismiss it 2 Inthe Select Samples dialog select the LCMS data files removing the first as described in section 5 1 and click OK 3 The program will ask for a folder to receive the peak list files In the Browse For Folder dialog locate a convenient folder for example the original LCMS Data folder click the Make New Folder button and change the name of the new folder to LCMS Peaks Click OK 4 Inthe Peak Finding Options dialog fill in the parameters as shown below Peak Finding Options Data ta Process Experiment Period 1 Experiment 1 hi Minimum retentian time min Maximum retention time E min Enhance Peak Finding Subtraction offset scans Minimum spectral peak width 5 ppm Subtraction mult factor E Minimum AT peak width 20 5 Noise threshold More if Assign Change States mea 5 Click
61. ers to use such manufacturers and or their product names as trademarks AB SCIEX makes no warranties or representations as to the fitness of this equipment for any particular purpose and assumes no responsibility or contingent liability including indirect or consequential damages for any use to which the purchaser may put the equipment described herein or for any adverse circumstances arising therefrom For research use only Not for use in diagnostic procedures The trademarks mentioned herein are the property of AB Sciex Pte Ltd or their respective owners AB SCIEX 1s being used under license AB SCIEX 71 Four Valley Dr Concord Ontario Canada L4K 4V8 AB SCIEX LP is ISO 9001 registered O 2010 AB SCIEX MarkerView 1 2 1 Software User Manual 2 1 Table of Contents Ll Table OF COMEePllSeeeisszstesecovextosastuzec amv oe axiuau ET E 2 introduction and typical WOFKITOW ierit vi EE Eoi HE vi HE EO E ERO EE E ERO RERO E He RO E E OTHER cea 3 Supervised processing or MALDI TOF 3 L Adata na DER Nee Drive m aM RM RU I ctu OSEE athe GaN a reed tak 3 2 Reviewing UleOdlbeusosessusdt cs Ribdie ux ote us ae ERE RUE TRE EE aioe RES FUN NEM LO OUR UI MUCIUS EDD U M EUM ME 3 3 Reviewing the samples and assigning groups eeeeeeeeenen nennen nennen nen nnns 10 3 4 Saving the data for later retrieval 1 eeieeeee eene nennen nnne nennen 11 3 5 Assigning a symbol for the groups
62. esponsible e There are also a number of variables that have negative PC1 and positive PC2 loadings the latter may contribute to the variation of the samples in the PC2 direction since some of them have large positive PC2 scores e Some variables tend to lie close to straight lines that pass through the origin for example the points labeled 1296 69 1297 69 and 1298 69 The behavior described in the last point arises because these points are correlated they are all isotopic forms of the same compound as indicated by the coloring and we used Pareto scaling which retains some of the intensity of the variable Since the peaks are correlated they will have very similar behavior on all PC s and thus lie on the same straight line but the actual loading value will depend on the intensity with the largest value having the biggest loading Hence we can say the following e Correlated peaks for example isotope peaks adducts fragments or multiply charged variants will have loadings such that the ratio of two PCs is the same i e for a given peak PC1 loading PC2 loading constant and will lie on a straight line through the origin e The most intense peaks will be in intensity order along this line with the most intense furthest from the origin So in this example 1296 69 is the most intense the peak containing one C atom is next most intense and the peak with two C atoms is least intense Note in many cases correlated varia
63. h the 0 8 hour samples having the highest PC1 scores the 8 16 having the next highest and the 16 24 hour samples being closest to the pre dose This suggests that the biggest change occurs in the first 8 hours and that the magnitude of the change lessens over time PC2 17 2 appears to separate the samples according to the sampling interval with the 0 8 samples pre and post dose having the most negative values and the other time points being MarkerView 1 2 1 Software User Manual 33 Revision February 2010 less well separated This suggests that there is a diurnal variation in the samples that is unaffected by administration of vinpocetin Click on the tab to display the loadings plot PC1 Loading versus PC2 Loading Loadings for PC1 56 5 versus PC2 17 2 Pareto LEMS Saved Scores for PC1 56 5 versus PC2 17 2 X Pareto LEMS Saved Loadings for PCT 56 5 versus PC2 17 2 22 Pareto LEMS Saved 7 5 Loadings for PC1 56 5 versus PCS 17 2 Pareto 381 1 11 3 10 Default Isotope 387 2711 3 384 Monoisotopic 5 gi 34 1211 3 111 302 1 10 4 258 2763 12 5 234 323 2 13 0 301 2531423 148 EON 358 2712 6 354 j 381 2 12 6 368 amp YT dw 8 393 2 13 0 396 gt SN s i 4 475 31 417 ha P gs Tae 20 30 E 2 00 11 7 240 s T 5 1480 21 1 65 4 ar 279 228 235 2521 128 183 1480 130 71
64. he above classes If the sort order is left as Sample Index the contamination peaks are very obvious and can be quickly excluded MarkerView 1 2 1 Software User Manual 63 Revision February 2010 Mean versus Median LCMS Saved Aeleg u El EJ Bp Q E3 EJ Index Peak Mame m z Het Time Group Use Charge Mano Mass Mass Defect Mean F 340 353 3720 7 340 353 2664 20 66 Manaisatopic 1 352 2585 0 266 miymepn womens D mansoa wawa us B 67e Hn ssmw azz 3558 1 ABR 3 1 P3441 SAR R81 gt 4 3 1 aha Fa PRA Ra 7192 1 hal 3543 3720 7 340 Hat3 1b 24h npa 5 Hat3 0 16 vinpa amp Rat D Bh vinpo A A Hat3 15 24h blk 8 16h Blk Hat3 D Bh A Bh blk amp Harl T5 24h Sample bv index J 55 C E EJ Mean versus Median 381 1 11 3 10 340 1 12 3 326 105 0 10 8 22 323 2 13 0 301 302 124 387 2711 3 384 4 266 1 12 8 208 4 300 6 4 Selecting discriminating t test variables The metric columns and plotting capabilities associated with the t test table provide a number of ways to asses the quality of variables and to select those that best differentiate groups 1 Open the MALDI data that was saved in section 3 4 make sure that the anomalous sample A9 MS 1 t2d is not used and
65. he loadings then indicate the direction of the new axes Each sample can be given a value on this new axis which is called the score so we can look at the way the samples are arranged according to this new axis The display obtained after performing PCA consists of 4 panes as numbered in the figure above 1 Atable of the scores for each sample and each PC the Scores Table 2 A plot of the sample scores for PC1 and PC2 The Scores Plot 3 Atable of the loadings contributions for each variable and each PC the Loadings Table 4 A plot of the loadings for PC1 and PC2 the Loadings Plot In the scores table 1 each of the PC s has a separate column and the heading indicates the percentage of the total variance that is explained by that particular PC In this case PC1 explains 71 6 of the variance PC2 7 4 and PC3 2 4 Each sample has a row showing the scores for that sample The scores plot 2 contains a point for each sample using the symbols assigned to the groups and defined earlier section 3 5 Several observations can be made from this plot e The samples are divided into two groups along PC1 the blue symbols group A have large positive PC1 scores and the red samples group C have large negative scores MarkerView 1 2 1 Software User Manual 20 Revision February 2010 e There is also some variation that is explained by PC2 and this seems to affect both groups in a similar manner This variance is however only 7 4
66. ift key down and make a second selection from the C samples Right click in one of the selection rectangles and select Spectra from the Show submenu fa o9 4 m AEMECTUR T WERE A10 51 t2d 8 ded i s 5 AB MS 1 t2d j AB MS Tied j j A3 MS l t2d ps j 77 MS 2 A3 Ma ted C1 MS C5 MS Lid i s LIU MS T t2d Display FH AS MS 104 AS MS Cl MS C4 M5 1 t2d 7 M5 10 C10 MS 1 t2d Spectra Sample by index Peak List 5pectra Don t Use Peaks For Analysis p Set Group for Peaks Add Active Peak bo Interest List The program will locate the original data files extract the spectra for the samples you have selected and zoom the display so that the active variable 904 47 in this case is centered in the display The colors are different for each sample to color them according to the group right click and select Display gt Use Group Colors for Traces a F i S5pectrumfromA3 5 1 t2d M5 7U0 6017 B Spectrum from 44 MS 1 r2d MS 700 BUT 7 Spectrum from amp 5 MS 1 MS 700 6017 Spectrum from 4 M5 1 t2d 5 700 6017 Spectrum from 5 MS 1 r2d MS 700 6017 Spectrum from C5 MS 7 ted M S 700 6017 E um c 2 1 302 38 1 903 41 1 aa aa 95601 904 905 Mass Charge Da You may wish to enlarge the graph so that the small peaks are easier to see This can be achieved in one of the following ways
67. ill update to show the scores and loadings for PC3 vs PC1 The separation due to PC1 is maintained but there is also some separation along PC3 and a suggestion of two groups one with positive PC3 scores and one with negative scores for both sample groups e Inthe scores or loadings table click the PC1 column heading and drag so that PC2 is also selected the original display will be restored 4 3 2 Excluding samples It is clear that sample A9 MS 1 t2d is unusual in some way and should be excluded from further calculations In section 3 8 we saw how to do this by deactivating it in the sample table here we will see how to do this from the scores plot 1 In the scores plot click and drag to make a selection rectangle around the abnormal sample 2 Right click within the selection rectangle and select Don t Use Selected Samples for Subsequent PCA L2 MS 1 r2d a 150 AJM 5_1 t2d zoom Selection Show Selected Points in Table Plot Peaks For Selected Samples Display Don t Use Selected Samples For Subsequent PEA Use Selected Samples Far Subsequent PEA MarkerView 1 2 1 Software User Manual 22 Revision February 2010 The sample symbol is replaced with an open circle This is the default symbol for excluded samples and variables and can be changed as described in section 3 5 by altering the symbol for the special group Excluded 3 Repeat the PCA analysis by selecting Perform PCA from the Analyze menu or by clicking on
68. in at least one sample It is important that the variables represent the same quantity in every sample This is straightforward if distinct quantities have been measured an example might be the intensity of a specific mass at a particular retention time but care must be taken if the variables are found in the data for example centroid masses or the mass and retention time of an LCMS peak since the same variable may be assigned slightly different values in different samples Ensuring that the variables are correctly assigned is known as alignment and is performed by the program as the data is imported Similarly it is also important to allow for differences in the values of the variables due to known or expected changes in the data for example different intensities of LCMS peaks due to differences in the amount injected or the response of the instrument This is known as normalization and is also performed during the import step If the data was obtained from known or suspected groups the samples may be assigned to these groups for supervised analysis or to allow better visualization of the results It is useful to be able to define different symbols for the groups so they may be easily recognized in subsequent plots and graphs Variations in the data can arise from several sources for example 1 Experimental variations due to changes in the instrument or experimental conditions 2 Variations that are real but not of interest fo
69. ing the results 1 Click in the heading of the p value column and click the Ascending sort button Ez Seles gt 8 8 3 Compare Ata t nl 10 n2 0 Index Peak Mame miz Ret Time Group t value value Mean 1 rod 974 56 974 572 NA 3 57 6 0 1 23183 2 Select the first row of the t test table by clicking in the area to the left of row 1 and click the Plot profile button l He 6 gt 8 Compare lA toC nl 10 n2 2 1U Index Peak Mame m z Het Time Use value p value Fad 374 56 3745572 soko nid E DS ISS8 SES i enses IE S0 s rites e ss e socis n oeae EI rss S 52 me 8er Wh 937 245ie8 7733 Des Jue lM ns 1364 RBBfle H 2 gt 4718 e 9 QE 7500 Ad MS 1r2d Al M5 1 t2d Ab M3 T tzd 1000 BE m Hesponse C1 MS 10 C4 MS 1t2d CB MS 12d L9 MS 1 t2d AA M5 ted ABMS Tied A8 M5 C1 MS C4 MS C7 MS ted 10 MS 1 t2d Sample bu index The resulting display shows how the value of the selected variable changes across all the samples the profile Since the data points are also labeled with the symbol defined in section 3 5 it is clear that this variable is indeed different for the two groups and higher in group A MarkerView 1 2 1 Software User Manual 14 Revision February 2010 The pea
70. inpo A sia 323 2 13 0 301 ie Rat 8 16h_vinpo A 3 a 2232 11 7 223 Rat3 1 E 24h vinpn 4 TEE ASF TER BETA 501 8 18 blk A Hat2 16 34h vinpa 4 D 2 Loading 173 7 290 2712 E Hat2 D Bh winpo 4 2511127 181 Sco n ai 350 1 12 4 336 M NE 180 1 80 8 100 Riat2 9 lk 8 Rat Blk A e 105 0 10 8 22 100 0 0 11 Score D 1 Loading Note that the labeling is now shown as D1 D2 etc in order to distinguish this type of analysis from normal PCA and that only five discriminants are needed In this particular example the grouping in the scores plot does not change greatly compare the figure above with the scores and loadings plots in section 5 3 However members of the individual groups are closer together and the separation between the 0 8 hr samples and all others is enhanced The loadings plot has changed to reflect the new processing but is interpreted as before By constructing artificial groups PCA DA can be used to determine and exclude variables that correspond to changes that are not relevant to your study for example the diurnal changes that result in the 0 8 hr samples being separated from the others 5 6 Summary In this section you have learned how to e Import LCMS data and perform sample alignment e Assign multiple groups and symbols to allow better visualization of the results e Perform a PCA analysis a
71. interest list If you click on a data point in any trace that trace will be made active i e it will appear at the top of the variable list at the left of the display and will be labeled You may remove it by selecting Remove Active Trace or add it to the list by selecting Add Active Peak to Interest List Removing traces in this way is useful if you have accidentally displayed a variable that is not relevant perhaps because its profile shows no variation Right click in the profile graph and select Add All Peaks to Interest List in the context menu MarkerView 1 2 1 Software User Manual 45 Revision February 2010 216 5 8 2 91 1 11 3 10 100 vinpa 5 Hat3 8 16h blk A Qe 134 1 1 3 111 B 15h bhlk 3 A Rats 16 24h blk 4 Q 387 2 11 5 384 Remove Active Trace 7 F 7 Display Show Don t Use Peaks For Analysis D Bh vinpo D 8h Blk Fiat 8 Bh blk Fats 15 24h blk 5 Set Group for Peaks aple by group Add Active Peak ba Interest List Add All Peaks to Interest List A dialog box will appear so that you may enter a comment when you click OK the variables and the comment will be added to the interest list Get Peak Comment Specify a comment or leave empty for nane Potentially Interesting Only show this dialog again if the shift key is down mea 12 From the View menu select Show Interest List You may manipulate the interest list
72. ion Peak Finding Options Data to Process Experiment Periad 1 Experiment 1 Minimum retention time 3 00 min Maximum retention time E pi min Enhance Peak Finding Subtraction offset f scans Minimum spectral peak width 5 ppm Subtraction mult factor E Minimum AT peak width scans Noise threshold 5 More i Assign Charge States mea In the Peak Finding Options dialog box set the parameters as follows Minimum retention time to 3 00 min to ignore the void volume Subtraction offset unchecked Minimum spectral peak width 5 ppm Noise threshold 5 Minimum RT peak width 20 scans Assign Charge States checked These settings will allow the program to find small narrow mass peaks that may be recombined during alignment These data were acquired using an unusually fast scan speed of 5 scans second so the LC peaks are wide in terms of scan numbers MarkerView 1 2 1 Software User Manual 28 Revision February 2010 5 Click OK The dialog for the second step of the import process appears Alignment amp Filtering Alignment Retention time tolerance 1 OO min Mass tolerance 25 ppm Filtering Remove peaks in gt samples Use exclusion list et I asimum number of peaks T Area Reporting Use area integrated fram raw data not from original peak finding Internal Standards Perform retention time correction et Perform sample normalizat
73. ion Jel Back ta Peak Finding Cancel Set the Retention time tolerance to 1 min and the Mass tolerance to 25 ppm peaks that are within these tolerance values either between files or within a single file will be aligned to the same peak Leave the filtering parameters unchecked and set the Maximum number of peaks to 8000 Uncheck Perform sample normalization and Perform retention time correction 6 Click OK Once the import process is complete the data table will appear The data table is similar to the one for spectra section 3 1 but now the retention time field is not empty and the peak name is constructed by combining the m z value and the retention time in minutes The name also contains an index value in brackets since it is sometimes easier to locate variables using this number You can review the data by selecting rows variables or columns samples and clicking on the plot column or plot row buttons at the top of the pane MarkerView 1 2 1 Software User Manual 29 Revision February 2010 Ratl B8h blk Rat 8 1 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 1 28880 1 46521 2 032e1 O 000e0 O 000e0 o 000e0 3 176e0 O 000e0 O 000e0 O 000e0 O 000e0 1 10521 4 600e1 5 38880 O 000e0 o 000e0 DODen O 000e0 O 000e0 2 146e0 D Bnet D net 2 045e 4 Hat D Bh vinpo 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 0 000e0 97 1712 7 19 970962 1273 00 0 000e0 0 000
74. ion Type f Constant Offset Linear Talerances Mass Tolerance 0 500 amu AT Tolerance 0 50 min Enter the m z values and retention times of the internal standards Retention Time min 2 When importing data you select Perform retention time correction and or Perform sample normalization and the data will be aligned and normalized as it is being read MarkerView 1 2 1 Software User Manual 57 Revision February 2010 Alignment amp Filtering gt Alignment Retention time tolerance 1 00 min Mass tolerance gt Filtering Remove peaks in gt samples Use exclusion list hd asimum number of peaks 8000 Area Reporting Use area integrated fram raw data not from original peak finding Internal Standards Perform retention time correction j Perform sample normalization 5 gt Back ta Peak Finding E Cancel The alignment process is described in more detail in the reference manual but if using more than one retention time standard it is best to have them well separated and use Linear offset With this mode the program will calculate the offset as a function of retention time standards that are close in time can cause the slope of this function to be incorrect While alignment can only be performed as the data is imported normalization with or without internal standards can be performed on an existing data table If you have used internal standards you can normalize the
75. is manual provides an overview of some of the most common processing operations a detailed description of the various commands menus and dialog boxes is contained in the Reference Manual The program uses multivariate analysis MVA techniques to compare the samples and provides both supervised and unsupervised methods Supervised methods use prior knowledge of the sample groups for example healthy vs diseased to determine the variables that distinguish the groups In contrast unsupervised methods allow the structure within the data to be determined and visualized The two approaches can be combined i e unsupervised methods can be used to determine the groups and then supervised methods can be used to confirm the important variables A typical workflow is shown below Data files Generate peaks files Import normalize and align Assign groups optional Analyze data Exclude variables Interpret results Further interpretation MVA requires that the initial data be in the form of an array hence the first step is importing the data to generate the array MarkerView 1 2 1 Software User Manual 4 Revision February 2010 Sample2 Sample 3 50061 _ Variabe2 etes The content of a cell represents the value of the appropriate variable in the sample and can be zero if the variable was not present The rows represent variables found
76. k Peak Alignment Clear Peak Alignment Indications Applv Global Exclusion List Make Peaks Appearing in Few Samples Unused States and Isabtapes put PME ul oe Assign narge Replace Zero Values Average Replicate Samples The following dialog appears Samples per group for first to last comparison E Remove samples marked as not used Only show this dialog again if the shift key is down LIE Cancel 2 Click OK t Test Hele 6 Compare Ea nl 10 n2 10 Row Index Peak Name m z Ret Time Group Use t value p value Mean 1 Mean 2 Median 1 Median 2 Sigma 1 Sigma 2 Delta Fold Change Log Fold Change 77 700 15 700 1481 N v 0 15 0 88577 7 320e1 7 0251 7 814e1 7 612e1 4 611e1 4 439e1 2200 1 042e0 1 785e 2 1 1 eo p me map 6 S mo moa s s ma maa as oun ase emoe 00000 1195e2 aere 22e 53e asea 3s3e 6 f os rores wa oos san 90 50 ester sastei p T ma C70 wA nu user soxe reser ioe see Seaver 11556 0 eu nmeonz w 4 s mem empa as 3 i E Les oes esee 000000 eser 7e 55e 45 ae pu pe me messe wA 08460 pn m renee ws a p p e roster wa DET afro 7539 V NA His e on Hs m es mseja pw mass Frese Ws
77. ks with nominal mass of 974 1298 1507 etc with high probabilities are in fact peaks from the spiked calibration standard This graph is automatically locked to the table so clicking in another row or using the arrow keys when the table is active will cause the graph to update to reflect the behavior of the new variable Note that the display reveals an anomalous sample the first sample at the lower intensity level is labeled as an A sample A9 MS 1 t2d even though its behavior is more similar to group C samples Apparently there is a problem with this sample or the name Removing the sample from future calculations will help to ensure that the values are correctly calculated 3 8 Inactivating a sample 1 Close all windows except the initial Peaks window that tabulates the data 2 From the View menu select Show Samples Table 3 In the Samples table locate the row containing sample A9 MS 1 and click the check box in the Use column so that it is unchecked How Index Peak Mame m z Het Time Group Hj A M5 1 t2d Az M5 12d 5 700 1481 N A b 0z0e O 000e0 1 pe me mee pu m nones E jp fpo teens h Manorsotapic Sample ID Group Acq Time Scale Factor 1 000e0 C MS Td v 1 000e0 4 Repeat the t test ensuring that Remove samples marked as not used is checked sort the results and regenerate the profile graph using the plot row icon to verify that the sample is no
78. lect the variables with the lowest p values for example less than 0 001 right click and select Don t Use Selected Peaks 6 Display the sample table select the Group column and type ctrl v This will restore the original group assignments Perform a PCA analysis MarkerView 1 2 1 Software User Manual 68 Revision February 2010 Scores Pareto LEMS Saved Ef m z Ret Time sample ID iy Peak Mame LCMS Datawif sa iE 8 i irren az E 2 01143017 e ia gt Sample M ame Hall Bh vinpa amp Ran N Ah hlk 4 7 BO Se HR Q ke Loadings for PC1 65 7 X versus PC2 13 5 3 Pareto Scores for PCT 65 7 X versus PC2 13 5 X Pareto Rat 15 24h vinpo 4 353 3 E0 7 340 i 4 i Default Isotope Monaisotapic 301 Rara 1624h bik A 381 3 21 2 363 Rat3 8348h vinpa A 354 3 20 6 343 1313 3 20 7 288 k L i 123 211 407 3252 2011 309 PC Loading Hat3 0 0 vinpa 4 Rat Bh vinpo A amp PC Score us pui E crees at oe 323 2 13 0 381 e Ratl 16 24h vinpo W f 20 08 D 8h blk A n 4 1490 51 62 50 0 50 100 150 00 01 02 03 04 PCT Score PC1 Loading Note that the scores and loading plots have changed and that the samples from rat 3 are now well separated in the positive PC2 direction The corresponding variables with large positive PC2 loadings are from the contamination that we no
79. les to be grouped in an automated way to facilitate data interpretation Follow these steps 1 First select Options from the Edit menu and define plot symbols for groups numbered 1 through 7 as shown in the figure below Note that you do not need to use identical symbols to those shown here provided that you can distinguish these groups MarkerView 1 2 1 Software User Manual 37 Revision February 2010 5 Options Flot Symbols Exclusion Import Export Clear Group Default bass fo mmm x z um wwe e 5 JM ws e mmm fama 2 Return the display to the state shown in step 4 above by closing the current window or by activating the previous window Ensure that the Loadings Plot is active 3 Select the PC Variable Grouping menu item from the Utilities sub menu of the Help menu as shown below MarkerView File Edit View Analyze Window pts a Aes Common Metabolites xls Example Report Template doc Reference Manual doc User Manual doc Utilities k Metabolite Mamer PC Variable Grouping The window shown below is presented 4 Fill in the parameters as shown in the figure In particular set the Number of PCs to 3 and de select the Only start a new group if PC with max loading is used checkbox 5 Click the Assign Groups button and close the window by clicking in its close box MarkerView 1
80. lity Reduction and Visualization in Principal Component Analysis Anal Chem 2008 80 13 pp 4933 4944 Which is available for download as a pdf file from http pubs acs org doi abs 10 1021 ac800110w T 58 De Loadings for PCT 56 5 2 versus PC2 17 2 Pareto 91 1711 8 10 Default 38721 3 384 1941 11 3 111 3028 12 4 268 246 1425 224 3232 1300 301 233 1 12 3 148 m 338 271 3 0 335 PL Loading ee FRE TH Bet 180 17 70 8 100 05 0 10 22 0 0 01 2 0 3 PL Loading MarkerView 1 2 1 Software User Manual 39 Revision February 2010 6 Use the magnifying glass tool A so that the Scores Plot is also visible 7 Select any variable in the Loadings Plot by drawing a selection box around it and click the Plot Profile button to generate a Profile Plot to display that arbitrary variable 8 Click on the color spot to the immediate left of the text for group 5 in the Loadings Plot you can also double click the 5 text itself The display should appear as shown below The Profile Plot will update so that all variables assigned to group 5 are overlaid This is a very similar display to that shown in step 5 for the previous section section 5 3 The main difference is that traces for a larger number of variables are overlaid since all group members are used rather than the subset which as chosen in the manual case loadings fo
81. lyze menu A new data table will be generated containing the now normalized values MarkerView 1 2 1 Software User Manual 58 Revision February 2010 4 Select Show Samples Table from the View menu and note that the Scale Factor column now contains a value for each sample Ideally these values will all be close to one indicating that the peaks used for the normalization were of comparable intensity in all samples If any of the values seems abnormally large you should check that the reference peak is present in that sample and has been selected correctly You may need to adjust the tolerances in the Normalization dialog 5 Perform a PCA analysis oadings for PC1 62 0 versus PC 13 0 Pareto LEMS Saved BAHA Sample Mame Sample IL Index Peak Mame m z Het Time amp Bh vinpa 4 LEMS Data wif sa 81 1 13 3 1 01 0637 13 32 Eg Riatl_O Sh_ Blk LEMS Data wiff sa Har 85h vinoo 4 LEMS Data wif sa gt fo OO ES 7 bo Ee Scores for PCT 62 0 2 versus PCS 13 0 x Pareto Loadings For PCT 62 0 X versus PC2 13 0 X Pareto Rat2 18 24 blk 4 11 3 10 Default Isotope 120 Monaisotapic 110 100 4 Rat3_16 24H_bik A gn an 70 302 1 12 4 268 148 0 8 0 68 ED a 053 3 20 7 340 AO Rat3_0 8h_vinpo A Pat Bh vinpa Fiat B hbh blk Rat2_8 16h_vinpo 4 E T 307 3 12 3 278 as i 3
82. n the row headers to the left of the Row column right click and select Set Group for Selected Samples In this case all of the samples in group A are beta galactosidase tryptic digests with calibrant spiked at a particular level Note if the Sample ID column contains the group information you may quickly copy it to the Group column by clicking the column heading hitting ctrl C to copy the column selecting the Group column and hitting ctrl V MarkerView 1 2 1 Software User Manual 10 Revision February 2010 Samples ud elegy Indes Peak Mame m z Het Time aroup Use 5 1 r2d AT M5 1 A M5 700 09 FOOSE M A 1 249e2 r 25e moos wa 3 3 iw E mm me EHi 82ee o Ez EBATE Monaisotapic 1 44762 1 137e2 Monaisatopic 1 263e2 D 00080 701 3518 N Manaisatapic iv 0 000e0 b 412e1 2 94121 Sample ID Group Use 0 Time Scale Factor AIO M5 1 tzd n HA Al_MS_1 t2d n HA A2 5 t A3 M5 1 t2d A4 M5 1 t2d A Don t Use Selected Samples 5 M5 1 tzd amp Sel AE MS_1 t2d A Use ONLY Selected Samples Pl EFIE Plot Peaks For Sample Af MS 1 t2d A Select Samples Far Matching Sample Names AB M5 1 t2d A Select Samples For Group AS MS 1 t2d Set Group for Selected Samples aa 10008 T0006 D 3 1 3 In the resulting Group Name dialog enter A for the group name and press OK OK Cancel 4 Select all of the samples with
83. names starting with C and repeat the process assigning C as the group name The samples in group C are beta galactosidase tryptic digests with calibrant spiked at a lower level than group A 5 Click the trash can icon in the sample table to remove it from the display 3 4 Saving the data for later retrieval It is often useful to save the imported data so that it can be reprocessed later without having to re import it since importing may be slow if there are many complex samples The group information you have just entered in the previous section will also be saved with the data 1 From the File menu select Save As select a folder to save the data enter a name and click OK The data will be saved in a file with the extension mrkvw In this example the file name is Saved MarkerView 1 2 1 Software User Manual 11 Revision February 2010 2 Toretrieve the data later select Open from the File menu locate the appropriate file and click OK 3 5 Assigning a symbol for the groups The results are easier to visualize if a unique symbol is associated with each group 1 From the Edit menu select Options MarkerView File View Analyze Window Help Copy Ctrl C Copy Transposed Copy Window Select All Columns Remove Trailing Characters From Groups Options In the Options dialog box select the Plot Symbols tab if it is not already selected Plot Symbols Exclusion Import Export Clear
84. nd interpret the results e Detect and exclude variables that appear to arise from a systematic experimental variation MarkerView 1 2 1 Software User Manual 47 Revision February 2010 Detect and exclude variables that appear to be xenobiotic metabolites a careful examination would require more detailed knowledge of the compound as well as its metabolic and fragmentation behavior Review the excluded peaks and copy them for further processing Add selected variables to an interest list for additional processing Back up to an earlier state and continue processing Use PCA DA to enhance the separation of known groups These sections have described the most common operations more advanced topics are covered in the following sections and more details on the various parameters dialogs etc can be found in the reference manual MarkerView 1 2 1 Software User Manual 48 Revision February 2010 6 Miscellaneous This section describes some of the many additional features of the MarkerView Software Tt assumes that you have worked through the rest of this manual so only new material is described in detail 6 1 Generating and importing Peaks files When you are working with large complex LCMS data sets the process of importing aligning and normalizing the data may be slow The program allows you to divide this into two separate steps so peak finding which is the slowest part need only be performed once and you can experim
85. nding Internal Standards Perform retention time correction el Perform sample normalization gi Back ta Peak Finding Cancel When the import process is complete a data table with a single sample column will be generated With the parameters given the table will contain 88 rows peaks Indes Peak Mame m z Het Time Use Hat D Bh vinpa 31 1 13 0 1 31 0512 12 38 5 ef esate sme un R89 5 amsa pas ze p amsa aoe rae Rss 1 s wwmos Www p pemza unu Vape r pemza zi a isne p femsa sm rae RM se s mni zn E xe Hs penson 68 06 T z8ee mo wewwom womens Rime wunmaen paes zs BR ine Hs pe Tec e meinzsnep meom 1294 Moweeond A 22He Hr ann i En 7 im 8 2 3 E B mo 203 1 13 0 19 2031181 12 97 isotope 5 B58el 210 1 13 0 20 2101238 12 97 3 287e1 218 1 12 3 21 218 0922 12 94 Pd 1 010e1 213 1 13 0 221 213 0385 satanel Iw 70 14 15 16 17 18 119 20 21 208 1 13 0 18 2081122 1297 Monaisatopic v 2 5 Close the table and re import the data using a Retention time tolerance of 0 5 min and a Mass tolerance of 10 p
86. ng 5 012 4 E Add Selected Peaks to Interest List 393 2 15 5 375 149 0 6 3 69 2 M 2 1 321 LE 1381 1 10 8 365 2 AOS 345 10 8 100 105 0 10 8 22 0 10 0 15 0 20 0 25 0 30 0 35 FL1 Loading MarkerView 1 2 1 Software User Manual 43 Revision February 2010 8 Select Show Excluded Peaks from the View menu and use the Truck icon to drag the resulting list so that it is alongside the loadings plot When you are dragging the list pane the edge of the loadings plot pane will turn red to indicate where it will be drawn Release the mouse button when the right edge of the plot is red and the list will be drawn in the correct position Excluded Peaks LCMS Saved gt gt 8 Loadings for PC1 52 4 versus PC2 18 6 Pareto Index Peak Name m z Ret Time Group Current 31 3 1 D efault Excluded 132 0 10 2 50 132 0429 10 17 L 1 1 ajamaa pemi pema t domann T tenere 35 p femm 45 de 1 p fema rao fee Li sab 112 4265 e oare Ll 25 ps s o 88 tL 6 s s pa 7 P dim m NT i Ho po emm wer oz Li 0 g mo n ear e 2066 Moroso 7 EDU M Hz p seme 34206 Ll T w p nasum wss me D E oss Hs s iaces ssamz DL MB 3381 421 32
87. o groups e Monoisotopic charge was successfully assigned and this peak has the lowest m z value of the isotope cluster e Isotope charge was assigned and this peak has a higher m z value than the monoisotopic peak If the group is blank then the charge state was not assigned probably because the peak was small and no other peaks with reasonable spacing could be identified A status bar at the bottom of the main window indicates how many samples and peaks were read in 20 and 2390 respectively in this example 20 Samples 2390 Peaks 0 Currently Excluded Peaks D Interest List Peaks 0 Previously Excluded Peaks 0 Globally Excluded Peaks The bar contains other fields that will be explained later 3 2 Reviewing the data You can use the controls in the toolbar at the top of the Peaks window to graphically examine the data before performing an analysis 1 Select any column by clicking in its title and click the Plot Column icon lu A plot of the data for that sample will appear beneath the table Note that this is not the raw mass spectrum but a plot of the most intense peaks found across all samples during the import process MarkerView 1 2 1 Software User Manual 8 Revision February 2010 A1_MS_1 12d Seles Thu Bl CR E ES Index Peak Mame m z Het Time Use A MS itd A2 MS t2d 700 15 700 1481 N A B D20e1 0 000e0 700 4328 NAA 1 36362 15 8 LA MS T t2d geld 1235 63 ded 1297
88. ots by clicking in the trash can icons in each pane 4 4 Interpreting the results So far we have learned that the variables with large positive PC1 loadings are mainly in the group A spiked samples and absent or at lower intensities in the group C samples But what causes some variables to have negative PC1 values What is the source of the variation displayed by PC2 Is it significant Since PC1 separates the two groups and variables with positive loadings are only in group A it seems likely that variables with negative loadings will only be in group C or at a lower intensity in group A We can verify this by displaying the profiles for some of these variables 1 Inthe loadings plot select three variables with the largest positive PC2 and negative PC1 loadings 1083 52 900 38 and 1299 64 and display the behavior of these variables by clicking the Plot profile button You may need to zoom in to do this The most intense trace appears to be more intense in group C but overall the plots suggest a gradual increase in intensity going from left to right This is also supported by the excluded peak open circle T T 9524 CB MS Td caes A9 MS 12d S 4 28 9 T mT 24 a lid Ag MSe1t2q aes i c a C4 MS 1 t2d A2 MS lid ASLMS_1 ted A10 MS 1i2d C M9 ltd bo uc 104 C7 we 112g A7 MS 12d 10 MS 1 t2d Response A2 85 Ah MS 1 r2d A8 M5 M5 1 tr2d L4 M5 1 t2d 7 M5 1 t2d M5
89. perform a t test The plot two columns button Uu allows you to select any two columns and plot one against the other but it also contains a combo box that is accessed via the small downward pointing arrow and provides quick access to some pre defined plots 2 Click the small arrow and select Plot Log Fold Change vs p value from the context menu t Test Saved v Tl zal 6 Compar Plot LogiFald Change vs p value Bor Plot Delta vs p value Plot Mean 1 vs Mean 2 This generates a plot that is similar to the one shown below MarkerView 1 2 1 Software User Manual 64 Revision February 2010 Log Fold Change versus p value for A to C Saved 8 8 3 Compare A ta nl23 nz 21U Peak Mame miz Ret Time Group tale p value Mean 1 Mean 2 r00 15 700 1481 H A 0 31315 6 81121 025e1 m a 95 oe 90 fee poe Wa EA mma mam p Reel mres ws Emu em p E U2 2480 H A 2 0 05177 1 430e1 Babe 071 8 8 3 Log Fold Change versus p value For A ta C Default Isotope Monoisatapic 1041 28 ge 336 20 1458 26 BUS 774 18 1210 28 813 12 83212 ee 506 22 Segoe S pne a 778 04 Nu oat 2 1219 87773 1018 20 855 95 oy w 111227 gg gi 6 UT 100323 Duc 797 12 5 Log Fold
90. pm The resulting table will contain 104 rows indicating that there are several peaks that are very close and were merged in the first operation 6 In order to see these peaks select Check Peak Alignment from the Analyze menu enter a Mass tolerance of 25 ppm and a Retention time tolerance of 1 min and click OK MarkerView 1 2 1 Software User Manual 53 Revision February 2010 TE MarkerView File Edit View FEWER Window Help Za 2 Slo ui Perform PCA Ckri A Compare Groups with t Test Ctrl T Normalization Check Peak Alignme nt Mass tolerance 25 0 ppm Retention time tolerance ron mn Check Peak Alignment Apply Global Exclusion List Make Peaks Appearing in Few Samples Unused Replace Zero Values Cancel Average Replicate Samples Rows in the table that are within these tolerance values will be highlighted in bold so you can locate them and determine if they are separate peaks or not 7 Scroll the table so that the rows containing the variables with m z 399 2 are visible In this case the m z values are very similar but the retention times are different by 0 79 min 47 sec 8 Select one of the cells in the only sample column right click and select Show XICs Seles Peak Mame Het Time Group Hat 0 8h_vinpo 323 2 130 82 12 95 Monoizotopic 3 115e3 8 323 2 132 83 323 1778 mS o Ts 8 094e1 89 8 32427130184 324 1761 5 62962 85 J85 3242 13118 324 1795 re re
91. ps pe meus on a a we osse aee fiae oomen oomen eater are 23er 27e 46 as 5 475e 2 Hs e ma rotae ws nszm viser vore iaee 1 96860 2592 s a es u p 4 43160 8 652e 1 2 The program automatically compares all groups in pairs and each group to all of the others the comparisons are accessed through the combo box labeled Compare at the top of the table In this case there are only two groups so the comparison is selected and the resulting table displayed The number of samples in each group 10 in both here is also displayed MarkerView 1 2 1 Software User Manual 13 Revision February 2010 For every variable the table displays the calculated t value the corresponding p value and various metrics for both groups such as the mean Mean 1 Mean2 the median the difference between the means Delta the fold change and the log of the fold change The t value is a measure of how well the variable distinguishes the two groups whereas the p value is the probability that the delta value would occur by chance If the value of t exceeds a calculated critical value then the variable does distinguish the groups with some confidence value t can be positive or negative depending on the direction of the subtraction The p value is always positive and the smaller the value the lower the probability that this is a chance occurrence 3 7 Review
92. r PC1 56 5 versus PC2 17 2 95 Pareto LEMS Saved Selle 88 E 7 ia HR De BOO 3 Scares for PCT 56 5 z versus PCS 17 2 4 Pareto Loadings for PCT 355 3 versus PCS 17 2 Pareto Ha 0 1 winpa A 31 1 3 10 D efault 284 1 3 Rat 16 24 vinpa A 1941 11 3 111 Rats 16 24h_ npo A elected 4 2h 105 0 10 8 22 1 2 3 4 5 b T PC Score PL Loading 8 a E a 4 0 2 PCI Score PC1 Loading Sts 105 0 108 22 Rati Bh blk A Rat3 D Bh vinpo A e 106 0210 24 E Rat 0 Bh blk A Rat D Bh bik A e 152 1 11 5 85 ce 50 vinpo 4 E Fd e 154 1 12 3 85 Hat3 8 1bh blk H 1811 07 i 04 Hat2 gh vinpo A Rat 8416h vingo A SDL E 2 3 1931 127 109 A a a TN 202 0710 7 115 E SAT inis HOS a e 218 1127 126 5 6st Gahi a T i 2191428 127 0 Ea a Lm m n ib 236 1 15 3 153 D 8h blk 4 Rat2_0 8h_vinpo 5 Rat 16 24h blk amp Rat3 15 24h vinpa 4 e 293 171 5 1 r1 aF Sample bu index SER a 4 g4r o 5 4 Working with the excluded and interest lists The variables we have looked at so far seem to show 1 a diurnal variation possibly somewhat suppressed in the post dose samples and 2 systematic variations that may be due to a contaminant We will now explore the variables with positive PC1 loadings that we believe are
93. r example male vs female subjects metabolites of a therapeutic agent etc 3 Relevant differences that reflect changes in the system being studied During processing the program allows variables of the first two types to be identified and excluded from further processing The excluded variables are tracked so that they can later be examined or used for other processing The program also allows interesting variables to be saved for later interpretation MarkerView 1 2 1 Software User Manual 5 Revision February 2010 3 Supervised processing of MALDI TOF data The data for this example consists of two sets of TOF MS spectra that were exported from the 4700 database in T2D format One set of spectra was obtained from the tryptic digest of a beta galactosidase digest the second set is from the same digest but spiked with a calibration standard The example illustrates importing and reviewing data and analyzing the data with a t test 3 1 Importing data 1 Select MALDI Spectra from T2D from the File lt Import menu MarkerView mim Edit View Analyze Window Help Create LC MS Peak Lists From wifF LCIMS Peak Lists peaks Open 0 LCIMS Data From wiff Recent Files MCA Spectra from wifF Save As 5 ALOI Spectra kfrom T20 Export Peaks Table to 4x00 LC MALDI Peak Lists From text MRM Chromatograms From wifF Page Setu 2 Mp wae ois Exported Analyst Results Table PRESS Text Spe
94. rom A to D For E the 8 16 hour intensity is greater than the 0 8 and greater still in panel F Thus the radial lines correspond to different variables that illustrate the different temporal behavior of the metabolites Vinpocetin fragments easily so many of the correlated ions are fragments formed in the orifice A good way to check the correlation is to generate profile plots and use a relative rather than absolute y axis 5 Delete any profile graphs click on the Home button fat in the loadings plot to restore the full view select some of the variables with the largest loadings in family C and generate the profile plot Click the button in the toolbar MarkerView 1 2 1 Software User Manual 42 Revision February 2010 L 00 Bd CQ 220 1 128 128 100 221 1128 131 234 1127 151 235 1128 152 0 2371 1277 156 i Rat3 15 24h vinpo A 238 1 12 8 158 E co 249 1 128 175 Ratl_16 24h_vinpo A 254 1 12 7 189 T 2 69 264 1 12 7 203 Hat3 D Bh vinpo amp Rat2 1 amp 24h vinpa Rat E IBh Rat3 1E 24h blk 8 D 281 1 12 7 241 Sample by group POA Rar 86h vinpo A The similarity of the graphs shows that they have similar behavior in the different samples i e they are well correlated as would be expected if they are all related In many cases we are interested in changes in the endogenous metabolites rather than the
95. ted earlier and can easily be excluded MarkerView 1 2 1 Software User Manual 69 Revision February 2010
96. ternal standard to allow the retention times to be corrected so smaller tolerances can be used 10 Click the trash can icon to delete the chromatogram window right click in any sample column cell and select Show lt Contour Drag in the x axis to select the 12 to 14 min range as imported and in the y axis to select a region roughly 5 amu wide around m z 400 If the color selection tools are not visible right click in the contour and select Show Color Selection Tools set the max value to 3 this will change the way color and intensity are mapped so that the smaller peaks are more visible MarkerView 1 2 1 Software User Manual 55 Revision February 2010 32 Contour Index Peak Mame m z Het Time Use Hat D 8h vinpa 4 84 3B2 1 12 5 84 362 0858 12 49 6 935e0 368 2 12 7 95 3681613 8 00 I E 412e1 Ll O 2 65 sine s wevizsge seien iewwe 5710 6 perum uu dC We Hep smznzenay settee ze swvisene swrxmz ner e 19e oz fo ssznzsuog soi 25 ms fwe assznixiney 399 1967 1334 A C dH Hat D 8h vinpa amp fram LEMS Data wif sample 2 m 22 pre hes sya ans Bild bea de nadata 401 0 400 5 io vidil iidem a 9000000000 encre ce i 400 0 PTT NETU
97. the one below MarkerView 1 2 1 Software User Manual 36 Revision February 2010 10 11 12 Loadings for PC1 56 5 versus PC 17 2 Pareto LEMS Saved Scores for PC1 56 5 versus PC 17 2 Pareto LEMS Saved Loadings for PCT 56 5 2 versus PC2 17 2 Pareto LEMS Saved gt Qe Loadings for PCT 56 5 versus PC2 17 2 x Pareto 81 1 11 3 10 D efault Excluded 387 2 11 3 384 Isotope Monoisatopic 134 1 11 3 111 e 302 1 12 4 258 276 1 12 5 224 368 2126 35 2331 i 148 377 2 2 7 4362 281 2712 5 368 340 307 1 12 3 278 3982 11 3 y A 132 010 2 50 404 2 11 3 400 oi 31123 45 301 3 21 2 363 a i i ps 3 0 245 PC Loading 149 0 4 1 70 8 a dee 354 3 20 6 343 oe amp 413321 1 407 4 75 7 2j 313 3 20 7 289 0 05 0 04 0 03 PC1 Loading Repeat the PCA analysis by clicking the PCA button in the toolbar at the top of the window The resulting scores and loadings plots are very similar to those obtained earlier the most obvious difference being that the variance explained by PC1 has increased to ca 61 the exact value will depend on the variables you excluded and the 16 24 hour post dose samples seem to be grouped slightly more tightly 5 3 1 Principal Component Variable Grouping Utility The MarkerView software includes a utility which allows variab
98. uary 2010 Since the small variables with minimal separation power have been removed the distinction between the groups and the variables responsible is now very clear 6 5 Combining t test and PCA In some cases the t test can be used to remove or select variables before PCA is performed For example in the LCMS data we have noticed that there is a significant diurnal variation and we may wish to remove the variables that segregate the 0 8 hour sample pre and post does from all the other samples One way to do this is to create one group for the 0 8 hour samples and a second group for all the other samples and use the t test to determine the distinguishing variables 1 Open the data table saved in section 5 2 as LCMS Saved and show the sample table by selecting View gt Show Samples Table 2 Click the heading of the Group column and select Edit gt Copy or type ctrl C This will copy the settings for this column to the clipboard so we can retrieve them later 3 Change all the 0 8 hour samples to be group 1 and the rest to group 2 Index Sample Name Sample ID Group Use Acg Time Scale Factor HT Correction Hat _O 8h_vinpo A LEMS Data wiff za Mov 10 2004 2 12 1 00080 Mone 1 1 Ratl_O 8h_bik A LEMS Data wiff sa Nov 10 2004 2 38 0 Rat amp 15h vinpo A LEMS Data wiff sa Nov 10 2004 3 08 8 15 bik A LEMS Data wiff sa Nov 10 2004 3 39 Ratl_16 24h vinpa amp LEMS Data wiff sa No
99. v 10 2004 4 09 1 000e0 B 16 24h bik A LEMS Data wiff sa Nov 10 2004 4 39 0 Rat D Bh vinpao A LEMS Data wiff sa Nov 10 2004 5 10 0 eB Rat2 D Bh blk A LEMS Data wiff za Nov 10 2004 5 40 1 00080 o ja Rat amp 15h vinpo A LEMS Data wiff sa Mov 10 2004 5 11 1 00080 Rat2 8 amp 15h bik A LEMS Data wiff sa Nov 10 2004 41 Rat 16 24h vinpo amp LEMS Data wilf sa Nov 10 2004 7 11 Rat2 165 248 bik 8 LEMS Data wiff sa Nov 10 2004 7 42 1 00080 Rat3 D Bh vinpao A LEMS Data wiff sa Nov 10 2004 amp 12 1 000e0 Rat3 D Bh blk amp LEMS Data wiff sa Nov 10 2004 amp 43 0 Rat3 B 1Eh vinpa A LEMS Data wiff za Nov 10 2004 513 1 000e0 Rat3 B 15h blk A LEMS Data wiff za Mov 10 2004 9 44 1 00780 Rat3 1E 24h vinpo A LEMS Data wiff sa Nov 10 2004 10 1 1 00080 Rat3 15 24h bik A LEMS Data wiff sa Nov 10 2004 10 4 4 Perform a t test the resulting table will contain one comparison indicating the variables that distinguish group 1 from group 2 The variables that best differentiate these two groups are likely those arising from the diurnal variation Sort the p value column in ascending order select the row with the lowest p value and click the Plot Profile button to review the profile of this variable Use the arrow keys to review some of the other top variables and notice that they are larger in one group than the other 5 Inthe t test result table se
100. xenobiotic metabolites arising from the dosed compound so we need to exclude the latter from the display these appear to be variables that have PC1 loadings greater than ca 0 05 6 Switch the profile display back to using an absolute scale by clicking the 9 o button again 7 Draw a selection rectangle that includes all of the variables with PC1 loading values greater than ca 0 005 The simplest way to do this is to start to the right and slightly above the point with the largest PC1 loading and drag towards the origin Right click and select Don t Use Selected Points for Subsequent PCA Note that the variables are now drawn with the excluded symbol and open blue circle by default oadings for PC1 62 4 versus PC2 18 6 Pareto LEMS Saved Seles Scores for PET 62 4 X versus PEZ 18 6 X Pareto LEMS Saved Loadings for PCT 62 4 2 versus PC2 18 5 Pareto LEMS Saved 1 Loadings for PCT B2 4 X versus PCS 18 5 X Pareto 381 1 11 3 10 i Default 0 35 1 Isotope 387 2 11 3 pan 36 Selected Monoisotopic 030 4113 E 0 25 302 1 12 4 258 020 1 zoom Selection ae 25 224 Show Selected Points in Table 5 POD 2 12 6 354 Plot Profiles For Selected Peaks 433 1 12 3 148 399 231 Display 0 10 Dont Use Selected Peaks Far Subsequent PCA ai 99 0 11 5 7 EN 381 21 2 358 Use Selected Peaks Far Subsequent PCA 4131 11 7 406 Use ONLY Selected Peaks For Subsequent PEA PC Loadi
101. y shape We will start by assigning the symbols first this saves some typing since the groups can then be assigned by selecting them from a menu 7 Select Options from the Edit menu You can either manually fill in the point symbols for the six groups as shown in the figure below or import them from a file included with the program To import symbols from the example file click the Import button and navigate to the AB Sciex MarkerView Sample Data LCMS Data subfolder of the Program Files folder in the resulting Open dialog Select the LCMS Plot Symbols ptsym file and then click OK to close Revision February 2010 the Options dialog MarkerView 1 2 1 Software User Manual 30 Flat Symbols Exclusion Import Export Clear Group Symbol s efault pcr 5 mmm x MENNN wwe e jum om 5 Ie SN We p Heo u p m Beo jp p m 8 From the View menu select Show Samples Table 9 In the sample table select the row containing the sample Rat1 O 8h vinpo A which corresponds to the 0 8 hour post dose sample from Rati 10 Hold the control key down and select the rows for Rat2 0 8h vinpo A and Rat3_0 8h vinpo A 11 Right click in the table and select Set Group for Selected Samples 12 In the Group Name dialog click on the pop up menu select 1 and click OK Sample Name Sample ID Group Name Scale Fa

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