Revision as of 21:28, 18 May 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.