Dahlquist:Microarray Data Analysis Workflow

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This is the most current version of the data analysis protocol for the Dahlquist Lab microarray data. We will perform this analysis as a group during Week 1 of SURP 2015.

Summary of steps for microarray data analysis

1. Quantitate the fluorescence signal in each spot (GenePix Pro)
2. Calculate the ratio of red/green fluorescence (GenePix Pro)
3. Log transform the ratios (GenePix Pro)
4. Normalize the ratios on each microarray slide (within-chip normalization)
5. Normalize the ratios for a set of slides in an experiment (between-chip normalization)
6. Perform statistical analysis on the ratios
   • Within-strain ANOVA
   • Modified t test for each timepoint
   • Between-strain ANOVA
   • Benjamini & Hochberg and Bonferroni p value corrections for the above three tests
   • "Sanity Check" on above three tests
7. Pattern finding algorithms (clustering with stem)
8. Gene Ontology term enrichment analysis (on clusters with stem or on gene sets with MAPPFinder)
9. Pathway analysis (GenMAPP)
10. Determining candidate transcription factors and gene regulatory network (YEASTRACT)
11. Dynamical modeling with GRNmap; visualization with GRNsight

Before you begin...

You will record all of the manipulations of the data in an electronic lab notebook stored on this wiki. Please see the Wiki Checklist page for more details on how to do this.

Viewing File Extensions

• The Windows 7 operating systems defaults to hiding file extensions. To turn them back on, do the following:

  

  Folder Options window

  1. Go to the Start menu and select "Control Panel".
  2. In the window that appears, search for "Folder Options" in the search field in the upper right hand corner.
  3. Click on "Folder Options" in the main window.
  4. When the Folder Options window appears, click on the View tab.
  5. Uncheck the box for "Hide extensions for known file types".
  6. Click the OK button.
• The computers in Seaver 120 are are set to erase all custom user settings and restore the defaults once they have been restarted, so you will probably have to do this many times throughout the semester when using these computers.

Set Your Browser to Prompt You for the Location to Save your Downloaded Files

• In Mozilla Firefox, open the Options window.
  • Select the radio button that says "Always ask me where to save files".
  • You could also change the default "Save files to" location to your Desktop, so that will be the first choice when it prompts you where to save the file. (You will have to temporarily deselect the radio button to do this and then reselect it when you are done.
  • Click OK to save your changes.
• In Google Chrome, open the Settings window.
  • Click on the link at the bottom of the page that says "Advanced Settings".
  • Check the box that says "Ask where to save each file before downloading".
  • You could also change the default Download location to your Desktop, so that will be the first choice when it prompts you where to save the file.
  • Your settings are automatically saved.

Steps 1-3: Generating Log2 Ratios with GenePix Pro

• The protocol for gridding and generating the intensity (log2 ratio) data with GenePix Pro 6.1 is found on this page.
• This protocol will generate a *.gpr file for each chip which is then fed into the normalization protocol below.

Steps 4-5: Within- and Between-chip Normalization

• A more detailed protocol can be found on this page. An abbreviated protocol is summarized below.

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 8 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 8 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 8 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.

Step 6: Statistical Analysis

• For the statistical analysis, we will begin with the file "GCAT_and_Ontario_Final_Normalized_Data.csv" that you generated in the previous step.
• Open this file in Excel and Save As an Excel Workbook *.xlsx. It is a good idea to add your initials and the date (yyyymmdd) to the filename as well.
• Rename the worksheet with the data "Compiled_Normalized_Data".
  • Type the header "ID" in cell A1.
  • Insert a new column after column A and name it "Standard Name". Column B will contain the common names for the genes on the microarray.
    • Copy the entire column of IDs from Column A.
    • Paste the names into the "Value" field of the ORF List <-> Gene List tool in YEASTRACT. Then, click on the "Transform" button.
    • Select all of the names in the "Gene Name" column of the resulting table.
    • Copy and paste these names into column B of the *.xlsx file. Save your work.
  • Insert a new column on the very left and name it "MasterIndex". We will create a numerical index of genes so that we can always sort them back into the same order.
    • Type a "1" in cell A2 and a "2" in cell A3.
    • Select both cells. Hover your mouse over the bottom-right corner of the selection until it makes a thin black + sign. Double-click on the + sign to fill the entire column with a series of numbers from 1 to 6189 (the number of genes on the microarray).
• Insert a new worksheet and call it "Rounded_Normalized_Data". We are going to round the normalization results to four decimal places because of slight variations seen in different runs of the normalization script.
  • Copy the first three columns of the "Compiled_Normalized_Data" sheet and paste it into the first three columns of the "Rounded_Normalized_Data" sheet.
  • Copy the first row of the "Compiled_Normalized_Data" sheet and paste it into the first row of the "Rounded_Normalized_Data" sheet.
  • In cell C2, type the equation =ROUND(Compiled_Normalized_Data!C2,4).
  • Copy and paste this equation in the rest of the cells of row 2.
  • Select all of the cells of row 2 and hover your mouse over the bottom right corner of the selection. When the cursor changes to a thin black "plus" sign, double-click on it to paste the equation to all the rows in the worksheet. Save your work.
• Insert a new worksheet and call it "Master_Sheet".
  • Go back to the "Rounded_Normalized_Data" sheet and Select All and Copy.
  • Click on cell A1 of the "Master_Sheet" worksheet. Select Paste special > Paste values to paste the values, but not the formulas from the previous sheet. Save your work.
  • There will be some #VALUE! errors in cells where there was missing data for genes that existed on the Ontario chips, but not the GCAT chips.
    • Select the menu item Find/Replace and Find all cells with "#VALUE!" and replace them with a single space character. Record how many replacements were made to your electronic lab notebook. Save your work.
• This will be the starting point for our statistical analysis below.

Within-strain ANOVA

• The purpose of the witin-stain ANOVA test is to determine if any genes had a gene expression change that was significantly different than zero at any timepoint.
• Each student in the lab group will be assigned one strain to analyze from this point forward.
  • Anu
  • Grace (not wt)
  • Kevin M.
  • Kevin W. (Δhap4 or Δswi4)
  • Monica (Δhap4 or Δswi4)
  • Natalie (not S. paradoxus)
  • Nicole
  • Tessa (not Δgln3)
  • Trixie

1. Create a new worksheet, naming it either "(STRAIN)_ANOVA" as appropriate. For example, you might call yours "wt_ANOVA" or "dHAP4_ANOVA"
2. Copy all of the data from the "Master_Sheet" worksheet for your strain and paste it into your new worksheet.
3. At the top of the first column to the right of your data, create five column headers of the form (STRAIN)_AvgLogFC_(TIME) where (STRAIN) is your strain designation and (TIME) is 15, 30, etc.
4. In the cell below the (STRAIN)_AvgLogFC_t15 header, type =AVERAGE(
5. Then highlight all the data in row 2 associated with (STRAIN) and t15, 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. Click on this cell and position your cursor at the bottom right corner. You should see your cursor change to a thin black plus sign (not a chubby white one). When it does, double click, and the formula will magically be copied to the entire column of 6188 other genes.
8. Repeat steps (4) through (8) with the t30, t60, t90, and the t120 data.
9. Now in the first empty column to the right of the (STRAIN)_AvgLogFC_t120 calculation, create the column header (STRAIN)_ss_HO.
10. In the first cell below this header, type =SUMSQ(
11. Highlight all the LogFC data in row 2 for your (STRAIN) (but not the AvgLogFC), press the closing paren key (shift 0),and press the "enter" key.
12. In the next empty column to the right of (STRAIN)_ss_HO, create the column headers (STRAIN)_ss_(TIME) as in (3).
13. Make a note of how many data points you have at each time point for your strain. For dHAP4, it will be "3", but for the wild type it will be "4" or "5". Count carefully. Also, make a note of the total number of data points. For dHAP4, this number will be 15, but for wt it should be 23 (double-check).
14. In the first cell below the header (STRAIN)_ss_t15, 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.
    • Upon completion of this single computation, use the Step (7) trick to copy the formula throughout the column.
15. Repeat this computation for the t30 through t120 data points. Again, be sure to get the data for each time point, type the right number of data points, and get the average from the appropriate cell for each time point, and copy the formula to the whole column for each computation.
16. In the first column to the right of (STRAIN)_ss_t120, create the column header (STRAIN)_SS_full.
17. In the first row below this header, type =sum(<range of cells containing "ss" for each timepoint>) and hit enter.
18. In the next two columns to the right, create the headers (STRAIN)_Fstat and (STRAIN)_p-value.
19. Recall the number of data points from (13): call that total n.
20. In the first cell of the (STRAIN)_Fstat column, type =((n-5)/5)*(<(STRAIN)_ss_HO>-<(STRAIN)_SS_full>)/<(STRAIN)_SS_full> and hit enter.
    • Don't actually type the n but instead use the number from (13).
    • Replace the phrase (STRAIN)_ss_HO with the cell designation.
    • Replace the phrase <(STRAIN)_SS_full> with the cell designation.
    • Copy to the whole column.
21. In the first cell below the (STRAIN)_p-value header, type =FDIST(<(STRAIN)_Fstat>,5,n-5) replacing the phrase <(STRAIN)_Fstat> with the cell designation and the "n" as in (13) with the number of data points total. Copy to the whole column.
22. Before we move on to the next step, we will perform a quick sanity check to see if we did all of these computations correctly.
    • 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.
    • Click on the drop-down arrow on your (STRAIN)_p-value column. 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.
    • Excel will now only display the rows that correspond to data meeting that filtering criterion. A number will appear in the lower left hand corner of the window giving you the number of rows that meet that criterion. We will check our results with each other to make sure that the computations were performed correctly.

Calculate the Bonferroni and p value Correction

1. Now we will perform adjustments to the p value to correct for the multiple testing problem. Label the next two columns to the right with the same label, (STRAIN)_Bonferroni_p-value.
2. Type the equation =<(STRAIN)_p-value>*6189, Upon completion of this single computation, use the Step (10) trick to copy the formula throughout the column.
3. Replace any corrected p value that is greater than 1 by the number 1 by typing the following formula into the first cell below the second (STRAIN)_Bonferroni_p-value header: =IF(r2>1,1,r2). Use the Step (10) trick to copy the formula throughout the column.

Calculate the Benjamini & Hochberg p value Correction

1. Insert a new worksheet named "(STRAIN)_B&H".
2. Copy and paste the "MasterIndex" and "ID" columns from your previous worksheet into the first two columns of the new worksheet.
3. For the following, use Paste special > Paste values. Copy your unadjusted p values from your ANOVA worksheet and paste it into Column C.
4. Select all of columns A, B, and C. Sort by ascending values on Column C. Click the sort button from A to Z on the toolbar, in the window that appears, sort by column C, smallest to largest.
5. Type the header "Rank" in cell D1. We will create a series of numbers in ascending order from 1 to 6189 in this column. This is the p value rank, smallest to largest. Type "1" into cell D2 and "2" into cell D3. Select both cells A2 and A3. Double-click on the plus sign on the lower right-hand corner of your selection to fill the column with a series of numbers from 1 to 6189.
6. Now you can calculate the Benjamini and Hochberg p value correction. Type (STRAIN)_B-H_p-value in cell E1. Type the following formula in cell E2: =(C2*6189)/D2 and press enter. Copy that equation to the entire column.
7. Type "STRAIN_B-H_p-value" into cell F1.
8. Type the following formula into cell F2: =IF(E2>1,1,E2) and press enter. Copy that equation to the entire column.
9. Select columns A through F. Now sort them by your MasterIndex in Column A in ascending order.
10. Copy column F 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: Number of genes significantly changed

Before we move on to further analysis of the data, we want to perform a more extensive sanity check to make sure that we performed our data analysis correctly. We are going to find out the number of genes that are significantly changed at various p value cut-offs.

• Go to your (STRAIN)_ANOVA worksheet.
• Select row 1 (the row with your column headers) and select the menu item Data > Filter > Autofilter (The funnel icon on the Data tab). 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.
• Click on the drop-down arrow for the unadjusted p value. Set a criterion that will filter your data so that the p value has to be less than 0.05.
  • How many genes have p < 0.05? and what is the percentage (out of 6189)?
  • How many genes have p < 0.01? and what is the percentage (out of 6189)?
  • How many genes have p < 0.001? and what is the percentage (out of 6189)?
  • How many genes have p < 0.0001? and what is the percentage (out of 6189)?
• When we use a p value cut-off of p < 0.05, what we are saying is that you would have seen a gene expression change that deviates this far from zero by chance less than 5% of the time.
• We have just performed 6189 hypothesis tests. Another way to state what we are seeing with p < 0.05 is that we would expect to see this a gene expression change for at least one of the timepoints by chance in about 5% of our tests, or 309 times. Since we have more than 309 genes that pass this cut off, we know that some genes are significantly changed. However, we don't know which ones. To apply a more stringent criterion to our p values, we performed the Bonferroni and Benjamini and Hochberg corrections to these unadjusted p values. The Bonferroni correction is very stringent. The Benjamini-Hochberg correction is less stringent. To see this relationship, filter your data to determine the following:
  • How many genes are p < 0.05 for the Bonferroni-corrected p value? and what is the percentage (out of 6189)?
  • How many genes are p < 0.05 for the Benjamini and Hochberg-corrected p value? and what is the percentage (out of 6189)?
• In summary, the p value cut-off should not be thought of as some magical number at which data becomes "significant". Instead, it is a moveable confidence level. If we want to be very confident of our data, use a small p value cut-off. If we are OK with being less confident about a gene expression change and want to include more genes in our analysis, we can use a larger p value cut-off.
• Comparing results with known data: the expression of the gene NSR1 (ID: YGR159C)is known to be induced by cold shock. Find NSR1 in your dataset. What is its unadjusted, Bonferroni-corrected, and B-H-corrected p values? What is its average Log fold change at each of the timepoints in the experiment? Note that the average Log fold change is what we called "STRAIN)_AvgLogFC_(TIME)" in step 3 of the ANOVA analysis.
• We will compare the numbers we get between the wild type strain and the other strains studied, organized as a table. Use this sample PowerPoint slide to see how your table should be formatted.

Modified t test for each timepoint

In the analysis above we performed an ANOVA to determine if any genes had a gene expression change that was significantly different than zero at any timepoint. Now we will perform a modified t test to determine if any genes had a gene expression change that was significantly different than zero at each timepoint. You will perform your analysis on the same strain that you did above, adding these calculations to the same Excel workbook.

• Insert a new worksheet into your Excel workbook and name it "(STRAIN)_ttest", e.g., "wt_ttest" or "dHAP4_ttest".
• Go back to the "Master_Sheet" worksheet for your strain, Select All and Copy. Go to your new "_ttest" worksheet, click on the upper, left-hand cell (cell A1) and Select "Paste Special" from the Edit menu. A window will open: click on the radio button for "Values" and click OK. This will paste the numerical result into your new worksheet instead of the equation which must make calculations on the fly.
  • There may be some non-numerical values in some of the cells in your worksheet. This is due to errors created when Excel tries to compute an equation on a cell that has no data. We need to go through and remove these error messages before going on to the next step. (This may have been taken care of in a previous step.)
  • Scan through your spreadsheet to find an example of the error message. Then go to the Edit menu and Select Replace. A window will open, type the text you are replacing in the "Find what:" field. In the "Replace with:" field, enter a single space character. Click on the button "Replace All" and record the number of replacements in your lab notebook.
• Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the average log fold changes that you will compute. Name them with the pattern <dHAP4>_<AvgLogFC>_<tx> where you use the appropriate text within the <> and where x is the time. For example, "dHAP4_AvgLogFC_t15".
• Compute the average log fold change for the replicates for each timepoint by typing the equation:

=AVERAGE(range of cells in the row for that timepoint)

into the second cell below the column heading. For example, your equation might read

=AVERAGE(C2:F2)

Copy this equation and paste it into the rest of the column.

• Create the equation for the rest of the timepoints and paste it into their respective columns. Note that you can save yourself some time by completing the first equation for all of the averages and then copy and paste all the columns at once.
• Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the T statistic that you will compute. Name them with the pattern <dHAP4>_<Tstat>_<tx> where you use the appropriate text within the <> and where x is the time. For example, "dHAP4_Tstat_t15". You will now compute a T statistic that tells you whether the normalized average log fold change is significantly different than 0 (no change in expression). Enter the equation into the second cell below the column heading:

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

For example, your equation might read:

=AVERAGE(C2:F2)/(STDEV(C2:F2)/SQRT(4))

(NOTE: in this case the number of replicates is 4. Be careful that you are using the correct number of parentheses.) Copy the equation and paste it into all rows in that column. Create the equation for the rest of the timepoints and paste it into their respective columns. Note that you can save yourself some time by completing the first equation for all of the T statistics and then copy and paste all the columns at once.

• Go to the empty columns to the right on your worksheet. Create new column headings in the top cells to label the P value that you will compute. Name them with the pattern <dHAP4>_<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)

For example, your equation might read:

=TDIST(ABS(AE2),3,2)

The number of degrees of freedom is the number of replicates minus one. Copy the equation and paste it into all rows in that column.

• As with the ANOVA, we encounter the multiple testing problem here as well.

Bonferroni Correction

• We need to perform the Bonferroni correction to each p value similar to what we did for the within-strain ANOVA.

Benjamini & Hochberg Correction

• We need to perform the Benjamini & Hochberg correction to each p value similar to what we did for the within-strain ANOVA.

Sanity Check

• We will also perform the "sanity check" as follows:
  • Determine how many genes have a p value < 0.05 at each timepoint.
  • 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.)
  • How many genes have B&H corrected p < 0.05?
  • How many genes have a Bonferroni corrected p < 0.05?
  • Use this sample PowerPoint slide to see how your table should be formatted.