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, type the letter "c" and press Enter

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, type the letter "c" and 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.

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, type the letter c and 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.

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, type the letter c and 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 plots, type the letter c and press enter.
• 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 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 two columns of the "Compiled_Normalized_Data" sheet and paste it into the first two 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.