Lab Notebook Kevin McGee

Lab Notebook Kevin_McGee

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SURP Schedule 2015

5/18/2015

Microarray Data analysis Workflow

1. Set browser to send downloads to Desktop
2. Followed the Protocal found on OpenWetWare:

Installing R 3.1.0 and the limma package

The following protocol was developed to normalize GCAT and Ontario DNA microarray chip data from the Dahlquist lab using the R Statistical Software and the limma package (part of the Bioconductor Project).

• The normalization procedure has been verified to work with version 3.1.0 of R released in April 2014 (link to download site) and and version 3.20.1 of the limma package ( direct link to download zipped file) on the Windows 7 platform.
  • Note that using other versions of R or the limma package might give different results.
  • Note also that using the 32-bit versus the 64-bit versions of R 3.1.0 will give different results for the normalization out in the 10^-13 or 10^-14 decimal place. The Dahlquist Lab is standardizing on using the 64-bit version of R.
• To install R for the first time, download and run the installer from the link above, accepting the default installation.
• To use the limma package, unzip the file and place the contents into a folder called "limma" in the library directory of the R program. If you accept the default location, that will be C:\Program Files\R\R-3.1.0\library (this will be different on the computers in S120 since you do not have administrator rights).

Running the Normalization Scripts

• Create a folder on your Desktop to store your files for the microarray analysis procedure.
• Download the zipped file that contains the .gpr files and save it to this folder (or move it if it saved in a different folder).
  • Unzip this file using 7-zip. Right-click on the file and select the menu item, "7-zip > Extract Here".
• Download the GCAT_Targets.csv file and Ontario_Targets_wt-dCIN5-dGLN3-dHAP4-dHMO1-dSWI4-dZAP1-Spar_20150514.csv files and save them to this folder (or move them if they saved to a different folder).
• Download the Ontario_Chip_Within-Array_Normalization_modified_20150514.R script and save (or move) it to this folder.
• Download the Within-Array_Normalization_GCAT_and_Merged_Ontario-GCAT_Between-Chip_Normalization_modified_20150514.R script and save (or move) it to this folder.

Within Array Normalization for the Ontario Chips

• Launch R x64 3.1.0 (make sure you are using the 64-bit version).
• Change the directory to the folder containing the targets file and the GPR files for the Ontario chips by selecting the menu item File > Change dir... and clicking on the appropriate directory. You will need to click on the + sign to drill down to the right directory. Once you have selected it, click OK.
• In R, select the menu item File > Source R code..., and select the Ontario_Chip_Within-Array_Normalization_modified_20150514.R script.
  • You will be prompted by an Open dialog for the Ontario targets file. Select the file Ontario_Targets_wt-dCIN5-dGLN3-dHAP4-dHMO1-dSWI4-dZAP1-Spar_20150514.csv and click Open.
  • Wait while R processes your files.

Within Array Normalization for the GCAT Chips and Between Array Normalization for All Chips

• These instructions assume that you have just completed the Within Array Normalization for the Ontario Chips in the section above.
• In R, select the menu item File > Source R code..., and select the Within-Array_Normalization_GCAT_and_Merged_Ontario-GCAT_Between-Chip_Normalization_modified_20150514.R script.
  • You will be prompted by an Open dialog for the GCAT targets file. Select the file GCAT_Targets.csv and click Open.
  • Wait while R processes your files.
• When the processing has finished, you will find two files called GCAT_and_Ontario_Within_Array_Normalization.csv and GCAT_and_Ontario_Final_Normalized_Data.csv in the same folder.
  • Save these files to LionShare and/or to a flash drive.

Visualizing the Normalized Data

Create MA Plots and Box Plots for the GCAT Chips

Input the following code, line by line, into the main R window. Press the enter key after each block of code.

GCAT.GeneList<-RGG$genes$ID

lg<-log2((RGG$R-RGG$Rb)/(RGG$G-RGG$Gb))

• If you get a message saying "NaNs produced" this is OK, proceed to the next step.

r0<-length(lg[1,])
rx<-tapply(lg[,1],as.factor(GCAT.GeneList),mean)
r1<-length(rx)
MM<-matrix(nrow=r1,ncol=r0)

for(i in 1:r0) {MM[,i]<-tapply(lg[,i],as.factor(GCAT.GeneList),mean)}

MC<-matrix(nrow=r1,ncol=r0)

for(i in 1:r0) {MC[,i]<-dw[i]*MM[,i]}

MCD<-as.data.frame(MC)
colnames(MCD)<-chips
rownames(MCD)<-gcatID

la<-(1/2*log2((RGG$R-RGG$Rb)*(RGG$G-RGG$Gb)))

• If you get these Warning messages, it's OK:

1: In (RGG$R - RGG$Rb) * (RGG$G - RGG$Gb) :

NAs produced by integer overflow

2: NaNs produced

r2<-length(la[1,])
ri<-tapply(la[,1],as.factor(GCAT.GeneList),mean)
r3<-length(ri)
AG<-matrix(nrow=r3,ncol=r2)

for(i in 1:r2) {AG[,i]<-tapply(la[,i],as.factor(GCAT.GeneList),mean)}

par(mfrow=c(3,3))

for(i in 1:r2) {plot(AG[,i],MC[,i],main=chips[i],xlab='A',ylab='M',ylim=c(-5,5),xlim=c(0,15))}
browser()

• Maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window. To continue with the rest of the code, press Enter.
  • To make sure that you save the clearest image, do not scroll in the window because a grey bar will appear if you do so.
• The next set of code is for the generation of the GCAT boxplots for the wild-type data.

x0<-tapply(MAG$A[,1],as.factor(MAG$genes$ID),mean)
y0<-length(MAG$A[1,])
x1<-length(x0)
AAG<-matrix(nrow=x1,ncol=y0)

for(i in 1:y0) {AAG[,i]<-tapply(MAG$A[,i],as.factor(MAG$genes$ID),mean)}

par(mfrow=c(3,3))

for(i in 1:y0) {plot(AAG[,i],MG2[,i],main=chips[i],xlab='A',ylab='M',ylim=c(-5,5),xlim=c(0,15))}
browser()

• Maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window. To continue with the rest of the code, press Enter.

par(mfrow=c(1,3))

boxplot(MCD,main="Before Normalization",ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')

axis(1,at=xy.coords(chips)$x,tick=TRUE,labels=FALSE)

text(xy.coords(chips)$x-1,par('usr')[3]-0.6,labels=chips,srt=45,cex=0.9,xpd=TRUE)

boxplot(MG2,main='After Within Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')

axis(1,at=xy.coords(chips)$x,labels=FALSE)

text(xy.coords(chips)$x-1,par('usr')[3]-0.6,labels=chips,srt=45,cex=0.9,xpd=TRUE)

boxplot(MAD[,Gtop$MasterList],main='After Between Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')

axis(1, at=xy.coords(chips)$x,labels=FALSE)

text(xy.coords(chips)$x-1,par('usr')[3]-0.6,labels=chips,srt=45,cex=0.9,xpd=TRUE)

• Maximize the window in which the plots have appeared. You may not want to actually maximize them because you might lose the labels on the x axis, but make them as large as you can. Save the plots as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window.

Create MA Plots and Box Plots for the Ontario Chips

Input the following code, line by line, into the main R window. Press the enter key after each block of code.

Ontario.GeneList<-RGO$genes$Name

lr<-log2((RGO$R-RGO$Rb)/(RGO$G-RGO$Gb))

• Warning message: "NaNs produced" is OK.

z0<-length(lr[1,])
v0<-tapply(lr[,1],as.factor(Ontario.GeneList),mean)
z1<-length(v0)
MT<-matrix(nrow=z1,ncol=z0)

for(i in 1:z0) {MT[,i]<-tapply(lr[,i],as.factor(Ontario.GeneList),mean)}

MI<-matrix(nrow=z1,ncol=z0)

for(i in 1:z0) {MI[,i]<-ds[i]*MT[,i]}

MID<-as.data.frame(MI)
colnames(MID)<-headers
rownames(MID)<-ontID

ln<-(1/2*log2((RGO$R-RGO$Rb)*(RGO$G-RGO$Gb)))

• Warning messages are OK:

1: In (RGO$R - RGO$Rb) * (RGO$G - RGO$Gb) :

NAs produced by integer overflow

2: NaNs produced

z2<-length(ln[1,])
zi<-tapply(ln[,1],as.factor(Ontario.GeneList),mean)
z3<-length(zi)
AO<-matrix(nrow=z3,ncol=z2)

for(i in 1:z0) {AO[,i]<-tapply(ln[,i],as.factor(Ontario.GeneList),mean)}

strains<-c('wt','dCIN5','dGLN3','dHAP4','dHMO1','dSWI4','dZAP1','Spar')

• After entering the call browser() below, maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window and press Enter for the next set of graphs to appear.
  • The last graph to appear will be the spar graphs.
  • The graphs generated from this code are the before Ontario chips
• Be sure to save the 9 graphs before moving on to the next step

for (i in 1:length(strains)) {
  st<-strains[i]
  lt<-which(Otargets$Strain %in% st)
  if (st=='wt') {
      par(mfrow=c(3,5))
  } else {
      par(mfrow=c(4,5))
  }
  for (i in lt) {
    plot(AO[,i],MI[,i],main=headers[i],xlab="A",ylab="M",ylim=c(-5,5),xlim=c(0,15))
  }
  browser()
} 

• To continue generating plots, press enter.

j0<-tapply(MAO$A[,1],as.factor(MAO$genes[,5]),mean)
k0<-length(MAO$A[1,])
j1<-length(j0)
AAO<-matrix(nrow=j1,ncol=k0)

for(i in 1:k0) {AAO[,i]<-tapply(MAO$A[,i],as.factor(MAO$genes[,5]),mean)}

• Remember, that after entering the call readline('Press Enter to continue'), maximize the window in which the graphs have appeared. Save the graphs as a JPEG (File>Save As>JPEG>100% quality...). Once the graphs have been saved, close the window and press Enter for the next set of graphs to appear.
  • Again, the last graphs to appear will be the spar graphs.
  • These graphs that are produced are for the after Ontario chips
• Again, be sure to save 9 graphs before moving on to the next part of the code.

for (i in 1:length(strains)) {
  st<-strains[i]
  lt<-which(Otargets$Strain %in% st)
  if (st=='wt') {
      par(mfrow=c(3,5))
  } else {
      par(mfrow=c(4,5))
  }
  for (i in lt) {
    plot(AAO[,i],MD2[,i],main=headers[i],xlab="A",ylab="M",ylim=c(-5,5),xlim=c(0,15))
  }
  browser()
}

• To continue generating plots, press enter.

for (i in 1:length(strains)) {
  par(mfrow=c(1,3))
  st<-strains[i]
  lt<-which(Otargets$Strain %in% st)
  if (st=='wt') {
      xcoord<-xy.coords(lt)$x-1
      fsize<-0.9
  } else {
      xcoord<-xy.coords(lt)$x-1.7
      fsize<-0.8
  }
  boxplot(MID[,lt],main='Before Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
  axis(1,at=xy.coords(lt)$x,labels=FALSE)
  text(xcoord,par('usr')[3]-0.65,labels=headers[lt],srt=45,cex=fsize,xpd=TRUE)
  boxplot(MD2[,lt],main='After Within Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
  axis(1,at=xy.coords(lt)$x,labels=FALSE)
  text(xcoord,par('usr')[3]-0.65,labels=headers[lt],srt=45,cex=fsize,xpd=TRUE)
  ft<-Otargets$MasterList[which(Otargets$Strain %in% st)]
  boxplot(MAD[,ft],main='After Between Array Normalization',ylab='Log Fold Change',ylim=c(-5,5),xaxt='n')
  axis(1,at=xy.coords(lt)$x,labels=FALSE)
  text(xcoord,par('usr')[3]-0.65,labels=headers[lt],srt=45,cex=fsize,xpd=TRUE)
  browser()
} 

• To continue generating the box plots, press enter.
  • You will have to save 9 plots before you have completed the procedure. The last box plot is for spar.
• Warnings are OK.
• Zip the files of the plots together and upload to LionShare and/or save to a flash drive.
  • 20151804_Kevin_M_GCATandOntario_Pics.zip

Statistical Analysis

• Added the standard name and the master index for all the terms.
• Saved Compiled_Normalized_Data sheet and made a new sheet called Rounded_Normalized_Data
  • ran computation

=ROUND(Compiled_Normalized_Data!D2,4)

for all data.

• Created Master Sheet, which has all knew data free of any computational functions
  • Copied and pasted numbers with special paste: values
• In Master Sheet:
  • Replaced #VALUE with a blank cell. There were 477 replacements

1. Created a new worksheet and named it"(dgln3_ANOVA)
2. Copied all of the data from the "Master_Sheet" worksheet for your strain and pasted it into the new worksheet.
3. At the top of the first column to the right of Spar_LogFC_t120-4 (FD), five column headers were created of the form dgln3_AvgLogFC_(TIME) where (TIME) is 15, 30, 60, 90, 120.
4. In the cell below the dgln3_AvgLogFC_t15 header, I typed =AVERAGE(
5. highlighted all the data in row 2 associated with dgln3_LogFC_t15 (AU2:AX2), press the closing paren key (shift 0),and press the "enter" key.
6. This cell now contains the average of the log fold change data from the first gene at t=15 minutes.
7. Clicked on this cell and position your cursor at the bottom right corner. Double clicked, and the formula was copied to the entire column of 6188 other genes.
8. Repeated steps (4) through (8) with the t30, t60, t90, and the t120 data.
9. Create the column header dgln3_ss_HO in cell FJ1.
10. In FJ2, I typed =SUMSQ(AU2:BN2)
11. In FK1, create the column headers dgln3_ss_(TIME) as in (3).
12. Make a note of how many data points you have at each time point for your strain.
    • 15:4
    • 30:4
    • 60:4
    • 90:4
    • 120:4
13. In FK2, type =SUMSQ(<range of cells for logFC_t15>)-<number of data points>*<AvgLogFC_t15>^2 and hit enter.
    • The phrase <range of cells for logFC_t15> should be replaced by the data range associated with t15.
    • The phrase <number of data points> should be replaced by the number of data points for that timepoint (either 3, 4, or 5).
    • The phrase <AvgLogFC_t15> should be replaced by the cell number in which you computed the AvgLogFC for t15, and the "^2" squares that value.
    • Actual Computation:=SUMSQ(AU2:AX2)-4*FE2^2
    • Upon completion of this single computation, copy the formula throughout the column.
14. Repeated this computation for the t30 through t120 data points.
15. In FP1, create the column header dgln3_SS_full.
16. In the first row below this header, type =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
    • Actual Computation: =SUM(FK2:FO2)
17. In the next two columns to the right, create the headers dgln3_Fstat and dgln3_p-value.
18. Recall the number of data points from (13): call that total n.
19. In the first cell of the dgln3_Fstat column, type =((n-5)/5)*(dgln3_ss_HO-dgln3_SS_full)/dgln3_SS_full and hit enter.
    • =((20-5)/5)*(FJ2-FP2)/FP2
    • Copy to the whole column.
20. In the first cell below the dgln3_p-value header, type =FDIST(<(dgln3)_Fstat>,5,n-5)

Calculate the Bonferroni and p value Correction

1. Labeled FS1 and FT1 dgln3_Bonferroni_p-value.
2. Type the equation =<dgln3_p-value>*6189, Upon completion of this single computation, copy the formula throughout the column.
3. Replaced any corrected p value that is greater than 1 by the number 1 by typing the following formula into FT2 =IF(r2>1,1,r2). Copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

1. Insert a new worksheet named "dgln3_B&H".
2. Copy and paste the "MasterIndex", "ID", and "Standard Name" columns from your previous worksheet into the first two columns of the new worksheet.
3. Copied unadjusted p values from ANOVA worksheet and pasted it into Column D.
4. Selected all of columns A, B, C, and D. Sorted by ascending values on Column D. Clicked the sort button from A to Z on the toolbar, sorted by column C, smallest to largest.
5. Typeed the header "Rank" in cell E1. Stretched this down to 6190.
6. Calculated the Benjamini and Hochberg p value correction. Typed dgln3_B-H_p-value in cell F1. Typed the following formula in cell F2: =(D2*6189)/E2 and copied that equation to the entire column.
7. Typed "dgln3_B-H_p-value" into cell G1.
8. Typed the following formula into cell G2: =IF(F2>1,1,F2) Copied that equation to the entire column.
9. Selected columns A through G. Sorted them by your MasterIndex in Column A in ascending order.
10. Copy column G and use Paste special > Paste values to paste it into the next column on the right of your ANOVA sheet.

• Upload the .xlsx file that you have just created to LionShare. Send Dr. Dahlquist an e-mail with the link to the file (e-mail kdahlquist at lmu dot edu).

Sanity Check

1. Click on cell A1 and click on the Data tab. Select the Filter icon (looks like a funnel). Little drop-down arrows should appear at the top of each column. This will enable us to filter the data according to criteria we set.
2. Click on the drop-down arrow on dgln3_p-value. Select "Number Filters". In the window that appears, set a criterion that will filter your data so that the p value has to be less than 0.05.
   • p<0.05=1856 (
   • p<0.01=1007
   • p<0.001=398
   • p<0.0001=121
   • Bonderroni = 20
   • B&H = 889

5/19/2015

Modified t test for each timepoint

1. Inserted a new worksheet into excel called "dgln3_ttest"
   • Copied over everything from "Master Sheet"
   • Removed all data not having to do with dgln3
2. Recalculated average log Fold changes for each timepoint
   • t15: =AVERAGE(D2:G2)
   • t30: =AVERAGE(H2:K2)
   • t60: =AVERAGE(L2:O2)
   • t90: =AVERAGE(P2:S2)
   • t120:=AVERAGE(T2:W2)

1. Created new column headings in AC1 named with the pattern dgln3_Tstat_t15
   • Entered the equation into the second cell below the column heading:

=AVERAGE(range of cells)/(STDEV(range of cells)/SQRT(number of replicates))

1. • Actually inputted
     • t15: =AVERAGE(D2:G2)/(STDEV(D2:G2)/SQRT(4))
     • t30: =AVERAGE(H2:K2)/(STDEV(H2:K2)/SQRT(4))
     • t60: =AVERAGE(L2:O2)/(STDEV(L2:O2)/SQRT(4))
     • t90: =AVERAGE(P2:S2)/(STDEV(P2:S2)/SQRT(4))
     • t120:==AVERAGE(T2:W2)/(STDEV(T2:W2)/SQRT(4))
   • Created a new column headings in AH1 and named them with the pattern dgln3_Pval_<tx> where you use the appropriate text within the <> and where x is the time. For example, "dHAP4_Pval_t15". In the cell below the label, enter the equation:

=TDIST(ABS(cell containing T statistic),degrees of freedom,2)

1. • Actually Inputted
     • t15=TDIST(ABS(AC2), 3, 2)
     • t30=TDIST(ABS(AD2), 3, 2)
     • t60=TDIST(ABS(AE2), 3, 2)
     • t90=TDIST(ABS(AF2), 3, 2)
     • t120=TDIST(ABS(AG2), 3, 2)

Bonferroni Correction

1. In AM1, made title dgln3_Bonferoni_Pvalue_t15
   • Created column heading similar for t30, t60, t90, and t120
   • In AM2, inputted the formula

=AH2*6189

Did this for 30,60, 90 and 120 as well

1. • Copied and pasted these throughout the columns

Benjamini & Hochberg Correction

1. Created a new sheet called dgln3_ttest_B-H
   • Copied and pasted Master index, ID, and Standard Name from the master sheet
2. Copied and did a special values paste for all timepoints of the unadjusted Pvalues into columns D-H
3. In the following steps, I will insert 3 coulmns in between each unadjusted Pvalue timepoint. Therfore, t30, t60, t90, and t120 will eventually be placed in columns H, L, P, and T respectively
4. Selected columns A_D and sorted by ascending numbers in D
5. Named Column E "Rank"
   • From E2 down, numbered the cells 1-6190
6. Named the next column Dgln3_ttest_B-H_t15 and inputted the following formula:

=(D2*6189)/E2

Copied this throughout the column

1. Named column G dgln3_B-H_Pval_t15 and inputted the formula:

=IF(F2<1,1,F2)

Copied this throughout the column

Sanity Check

1. How many genes have a Pvalue<0.05 at each timepoint?
   • 15: 1027 (16.59%)
   • 30: 1622 (26.21%)
   • 60: 628 (10.15%)
   • 90: 558 (9.11%)
   • 120: 403 (6.51%)
2. Keeping the "Pval" filter at p < 0.05, How many have an average log fold change of > 0.25 and p < 0.05 at each timepoint? How many have an average log fold change of < -0.25 and p < 0.05 at each timepoint? (These log fold change cut-offs represent about a 20% fold change in expression.)
   • LogFC > 0.25
     • 15: 559 (9.03%)
     • 30: 897 (14.49%)
     • 60: 319 (5.15%)
     • 90: 265 (4.28%)
     • 120: 190 (3.07%)
   • LogFC < -0.25
     • 15: 458 (7.4%)
     • 30: 711 (11.49%)
     • 60: 304 (4.91%)
     • 90: 289 (4.67%)
     • 120: 207 (3.34%)
   • How many genes have B&H corrected p < 0.05?
     • 15: 0 (0%)
     • 30: 5 (0.08%)
     • 60: 0 (0%)
     • 90: 0 (0%)
     • 120: 0 (0%)
   • How many genes have a Bonferroni corrected p < 0.05?
     • 15: 0 (0%)
     • 30: 1 (0.016%)
     • 60: 0 (0%)
     • 90: 0 (0%)
     • 120: 0 (0%)
   • Use this sample PowerPoint slide to see how your table should be formatted.

Here is link to download my excel file including all data including ANOVA and ttest KevinM_GCAT_and_Ontario_Final_Normalized_Data.xlsx

Here is the Link to download my powerpoint slide including all pvalue data for gln3 Sanity Check dGLN3 Kevin McGee

Between-strain ANOVA

The detailed description of how this is done can be found on this page. A brief version of the protocol appears below.

• All two strain comparisons were performed in MATLAB using the script Two_strain_compare_corrected_20140813_3pm.zip (within a zip file):
  • Download the zipped script file, extract it to the folder that contains your Excel file with the worksheet named "Master_Sheet". (The script and Excel file must be in the same folder to work.)
  • Launch MATLAB version 2014b.
  • In MATLAB, navigated to the folder "Microarray analysis" containing the script and the Excel file.
    • Near the top of the page, you will see a a field that contains the path to the working directory. Just to the left of it, there is an icon that looks like a folder opening with a green down arrow. Click on this icon to open a dialog box where you can choose the "Microarray analysis" folder containing the script and Excel file.
    • Once you have selected your folder, the left-hand pane should display the contents of that folder. To open the MATLAB script, you can double-click on it from that pane. The code for the script will appear in the center pane.
• You will need to make a few edits to the code, depending on which strain comparison you want to make.
  • For the first block of code, the user must input KevinM_GCAT_and_Ontario_Final_Normalized_Data.xlsx to be imported as the variable "filename", the sheet from which the data will be imported as the variable "sheetname" (Master_sheet), and the two strains that will be compared as the variables "wt" and "dGLN3".

• The user does not have to modify any of the code from here on.
• The next two lines of code ask the user whether or not they would like to see plots for each gene with an unadjusted p-value < 0.05. If the user does want to see these plots, they enter "1". If they would not like to see these plots, the user enters "0". When prompted, enter a "1" to see the plots displayed.

1. Matlab will produce two files:
   • mat
   • xls

upload these to lionshare

Step 7-8: Clustering and GO Term Enrichment with stem

1. • Inserted a new worksheet into your Excel workbook, and named it "dgln3_stem".
   • Selected all of the data from "dgln3_ANOVA" worksheet and Paste special > paste values into dgln3_stem worksheet.
     • Renamed Master List this column to "SPOT" and ID to "Gene Symbol". Deleted "StandardName".
     • Filtered the data on the B-H corrected p value to be > 0.05
       • Once the data had been filtered, selected all of the rows and deleted the rows by right-clicking and choosing "Delete Row" from the context menu. Undid the filter.
     • Deleted all of the data columns except for the Average Log Fold change columns for each timepoint.
     • Renamed the data columns with just the time and units
       • 15m
       • 30m
       • 60m
       • 90m
       • 120m
     • Saved. Used Save As to save this spreadsheet as Text (Tab-delimited) *.txt file.
2. Downloaded and extracted the STEM software.
   • STEM web site.
   • download link for STEM Software
   • Launched the STEM program
3. Running STEM
   1. In section 1 (Expression Data Info) of the the main STEM interface window, click on the Browse... button to navigate to and select the file KevinM_GCAT_and_Ontario_Final_Normalized_Data_Stem.txt
      • Click on the radio button No normalization/add 0.
      • Check the box next to Spot IDs included in the data file.
   2. In section 2 (Gene Info) of the main STEM interface window, select Saccharomyces cerevisiae (SGD), from the drop-down menu for Gene Annotation Source. Select No cross references, from the Cross Reference Source drop-down menu. Select No Gene Locations from the Gene Location Source drop-down menu.
   3. In section 3 (Options) of the main STEM interface window, make sure that the Clustering Method says "STEM Clustering Method" and do not change the defaults for Maximum Number of Model Profiles or Maximum Unit Change in Model Profiles between Time Points.
   4. In section 4 (Execute) click on the yellow Execute button to run STEM.

Ran into a problem with java timing out, STEM was not working

After investigation, it seems the problem is happening because the Gene Ontology site is down

1. Viewing and Saving STEM Results
   1. A new window will open called "All STEM Profiles (1)". Each box corresponds to a model expression profile. Colored profiles have a statistically significant number of genes assigned; they are arranged in order from most to least significant p value. Profiles with the same color belong to the same cluster of profiles. The number in each box is simply an ID number for the profile.
      • Click on the button that says "Interface Options...". At the bottom of the Interface Options window that appears below where it says "X-axis scale should be:", click on the radio button that says "Based on real time". Then close the Interface Options window.
      • Take a screenshot of this window (on a PC, simultaneously press the Alt and PrintScreen buttons to save the view in the active window to the clipboard) and paste it into a PowerPoint presentation to save your figures.
   2. Click on each of the SIGNIFICANT profiles (the colored ones) to open a window showing a more detailed plot containing all of the genes in that profile.
      • Take a screenshot of each of the individual profile windows and save the images in your PowerPoint presentation.
      • At the bottom of each profile window, there are two yellow buttons "Profile Gene Table" and "Profile GO Table". For each of the profiles, click on the "Profile Gene Table" button to see the list of genes belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, "dgln3_profile#_genelist.txt", where you replace the number symbol with the actual profile number.
      1. • dgln3_44_genelist.txt
         • dgln3_24_genelist.txt
         • dgln3_41_genelist.txt
         • dgln3_39_genelist.txt
         • dgln3_19_genelist.txt
         • dgln3_9_genelist.txt
         • dgln3_6_genelist.txt

1. 1. • • Upload these files to LionShare and e-mail a link to Dr. Dahlquist.
      • For each of the significant profiles, click on the "Profile GO Table" to see the list of Gene Ontology terms belonging to the profile. In the window that appears, click on the "Save Table" button and save the file to your desktop. Make your filename descriptive of the contents, e.g. "dgln3_profile#_GOlist.txt". At this point you have saved all of the primary data from the STEM software and it's time to interpret the results!
      1. • dgln3_44_GOlist.txt
         • dgln3_41_GOlist.txt
         • dgln3_6_GOlist.txt
         • dgln3_9_GOlist.txt
         • dgln3_14_GOlist.txt
         • dgln3_19_GOlist.txt
         • dgln3_24_GOlist.txt
         • dgln3_39_GOlist.txt
      • • Upload these files to LionShare and e-mail a link to Dr. Dahlquist.

Step 9: GenMAPP & MAPPFinder

Preparing the Input File for GenMAPP

• Insert a new worksheet and name it dgln3_GenMAPP.
• Go back to the "ANOVA" worksheet for your strain and Select All and Copy.
• Go to your new sheet and click on cell A1 and select Paste Special, click on the Values radio button, and click OK.
  • Delete the columns containing the "ss" calculations, just retaining the individual log fold change data, the average log fold change data, the Fstat and p value. For the Bonferroni and B&H p values, just keep one column where we replaced all values > 1 with 1.
• Now go to your "dgln3_ttest" worksheet. Copy just the columns containing the Fstats and P values for the individual timepoints and Paste special > Paste values into your GenMAPP worksheet to the right of the previous data. For the Bonferroni and B&H p values, just keep one column where we replaced all values > 1 with 1.
• Select all of the columns containing Fold Changes. Select the menu item Format > Cells. Under the number tab, select 2 decimal places. Click OK.
• Select all of the columns containing T statistics or P values. Select the menu item Format > Cells. Under the number tab, select 4 decimal places. Click OK.
• We will now format this file for use with GenMAPP.
  • Currently, the "MasterIndex" column is the first column in the worksheet. We need the "ID" column to be the first column. Select Column B and Cut. Right-click on Cell A1 and select "Insert cut cells". This will reverse the position of the columns.
  • Insert a new empty column in Column B. Type "SystemCode" in the first cell and "D" in the second cell of this column. Use our trick to fill this entire column with "D".
  • Make sure to save this work as your .xlsx file. Now save this worksheet as a tab-delimited text file for use with GenMAPP in the next section.

Running GenMAPP

Each time you launch GenMAPP, you need to make sure that the correct Gene Database (.gdb) is loaded.

• Look in the lower left-hand corner of the window to see which Gene Database has been selected.
• If you need to change the Gene Database, select Data > Choose Gene Database. Navigate to the directory C:\GenMAPP 2 Data\Gene Databases and choose the correct one for your species.
• For the exercise today, if the yeast Gene Database is not present on your computer, you will need to download it. Click this link to download the yeast Gene Database.
• Unzip the file and save it, Sc-Std_20060526.gdb, to the folder C:\GenMAPP 2 Data\Gene Databases.

GenMAPP Expression Dataset Manager Procedure

• Launch the GenMAPP Program. Check to make sure the correct Gene Database is loaded.
• Select the Data menu from the main Drafting Board window and choose Expression Dataset Manager from the drop-down list. The Expression Dataset Manager window will open.
• Select New Dataset from the Expression Datasets menu. Select the tab-delimited text file that you formatted for GenMAPP (.txt) in the procedure above from the file dialog box that appears.
• The Data Type Specification window will appear. GenMAPP is expecting that you are providing numerical data. If any of your columns has text (character) data, check the box next to the field (column) name.
  • The column StandardName has text data in it, but none of the rest do.
• Allow the Expression Dataset Manager to convert your data.
  • This may take a few minutes depending on the size of the dataset and the computer’s memory and processor speed. When the process is complete, the converted dataset will be active in the Expression Dataset Manager window and the file will be saved in the same folder the raw data file was in, named the same except with a .gex extension; for example, MyExperiment.gex.
  • A message may appear saying that the Expression Dataset Manager could not convert one or more lines of data. Lines that generate an error during the conversion of a raw data file are not added to the Expression Dataset. Instead, an exception file is created. The exception file is given the same name as your raw data file with .EX before the extension (e.g., MyExperiment.EX.txt). The exception file will contain all of your raw data, with the addition of a column named ~Error~. This column contains either error messages or, if the program finds no errors, a single space character.
    • Record the number of errors in your lab notebook.
• Customize the new Expression Dataset by creating new Color Sets which contain the instructions to GenMAPP for displaying data on MAPPs.
  • Color Sets contain the instructions to GenMAPP for displaying data from an Expression Dataset on MAPPs. Create a Color Set by filling in the following different fields in the Color Set area of the Expression Dataset Manager: a name for the Color Set, the gene value, and the criteria that determine how a gene object is colored on the MAPP. Enter a name in the Color Set Name field that is 20 characters or fewer. You will have one Color Set per strain per time point.
  • The Gene Value is the data displayed next to the gene box on a MAPP. Select the column of data to be used as the Gene Value from the drop down list or select [none]. We will use "Avg_LogFC_" for the the appropriate time point.
  • Activate the Criteria Builder by clicking the New button.
  • Enter a name for the criterion in the Label in Legend field.
  • Choose a color for the criterion by left-clicking on the Color box. Choose a color from the Color window that appears and click OK.
  • State the criterion for color-coding a gene in the Criterion field.
    • A criterion is stated with relationships such as "this column greater than this value" or "that column less than or equal to that value". Individual relationships can be combined using as many ANDs and ORs as needed. A typical relationship is

[ColumnName] RelationalOperator Value

with the column name always enclosed in brackets and character values enclosed in single quotes. For example:

[Fold Change] >= 2
[p value] < 0.05
[Quality] = 'high'

This is the equivalent to queries that you performed on the command line when working with the PostgreSQL movie database. GenMAPP is using a graphical user interface (GUI) to help the user format the queries correctly. The easiest and safest way to create criteria is by choosing items from the Columns and Ops (operators) lists shown in the Criteria Builder. The Columns list contains all of the column headings from your Expression Dataset. To choose a column from the list, click on the column heading. It will appear at the location of the cursor in the Criterion box. The Criteria Builder surrounds the column names with brackets.

The Ops (operators) list contains the relational operators that may be used in the criteria: equals ( = ) greater than ( > ), less than ( < ), greater than or equal to ( >= ), less than or equal to ( <= ), is not equal to ( <> ). To choose an operator from the list, click on the symbol. It will appear at the location of the insertion bar (cursor) in the Criterion box. The Criteria Builder automatically surrounds the operators with spaces. The Ops list also contains the conjunctions AND and OR, which may be used to make compound criteria. For example:

[Fold Change] > 1.2 AND [p value] <= 0.05

Parentheses control the order of evaluation. Anything in parentheses is evaluated first. Parentheses may be nested. For example:

[Control Average] = 100 AND ([Exp1 Average] > 100 OR [Exp2 Average] > 100)

Column names may be used anywhere a value can, for example:

[Control Average] < [Experiment Average]

• After completing a new criterion, add the criterion entry (label, criterion, and color) to the Criteria List by clicking the Add button.
  • For the yeast dataset, you will create two criterion for each Color Set. "Increased" will be [<strain>_Avg_LogFC_<timepoint>] > 0.25 AND [<strain>Pval_<timepoint>] < 0.05 and "Decreased will be [<strain>_Avg_LogFC_<timepoint>] < -0.25 AND [<strain>Pval_<timepoint>] < 0.05. Make sure that the increased and decreased average log fold change values match the timepoint of the Color Set.
  • You may continue to add criteria to the Color Set by using the previous steps.
    • The buttons to the right of the list represent actions that can be performed on individual criteria. To modify a criterion label, color, or the criterion itself, first select the criterion in the list by left-clicking on it, and then click the Edit button. This puts the selected criterion into the Criteria Builder to be modified. Click the Save button to save changes to the modified criterion; click the Add button to add it to the list as a separate criterion. To remove a criterion from the list, left-click on the criterion to select it, and then click on the Delete button. The order of Criteria in the list has significance to GenMAPP. When applying an Expression Dataset and Color Set to a MAPP, GenMAPP examines the expression data for a particular gene object and applies the color for the first criterion in the list that is true. Therefore, it is imperative that when criteria overlap the user put the most important or least inclusive criteria in the list first. To change the order of the criteria in the list, left-click on the criterion to select it and then click the Move Up or Move Down buttons. No criteria met and Not found are always the last two positions in the list.
• You will also create two ColorSets to view the ANOVA p values for both strains, with criteria for viewing the unadjusted, Bonferroni-corrected, and B&H corrected p values.
• Save the entire Expression Dataset by selecting Save from the Expression Dataset menu. Changes made to a Color Set are not saved until you do this.
• Exit the Expression Dataset Manager to view the Color Sets on a MAPP. Choose Exit from the Expression Dataset menu or click the close box in the upper right hand corner of the window.
• Upload your .gex file to Lionshare and share it with Dr. Dahlquist. E-mail the link to the file to Dr. Dahlquist.
• Dr. Dahlquist will provide a set of MAPPs with which to view your Expression Dataset.
  • Links to a zipped archive of MAPPs and an Expression Dataset have been e-mailed to your lion e-mail accounts.