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# R functions are surrounded by round brackets (). You can find the definition of any # function by ?functionname - e.g. ?read.Galfile # load the marray library library(marrayNorm) # read the gal file galinfo=read.Galfile("7Kv2a.gal") # get the names of the gpr files (or spot files) in the directory fnames=dir(pattern=paste("*","gpr",sep="*")) # to see the names, just type fnames # obtain the name of the column used to flag bad spots (default is NULL) flags="Flags" # read gpr files into a rawData set (name.Gf is the excel column name of the # green foreground, etc, "W" indicates the Flags column) rawData= read.GenePix(fnames, name.Gf = "F532 Median", name.Gb ="B532 Median", name.Rf = "F635 Median", name.Rb = "B635 Median", name.W ="Flags", layout =galinfo$layout, gnames = galinfo$gnames)
#plot red and green foreground and background, and the flag locations. # "M" is Rf-Gf # [,i] indicates the i-th column of a matrix par(ask=T) for (i in 1:length(fnames)){ par(mfrow=c(2,3)) maImage(rawData[,i],x="maGb",bar=F)
maImage(rawData[,i],x="maW", bar=F) } # or get a larger view (with a scale) one plot at a time. maImage(rawData[,1],x="maGb") maImage(rawData[,1],x="maW") # Fix mistake in GAL file - I edited the gal file in Word and then reread all the data galinfo=read.Galfile("7Kv2aR1.gal") # create a list of gpr files (or spot file) fnames=dir(pattern=paste("*","gpr",sep="*")) # obtain the name of the column used to flag bad spots (default is NULL) flags="Flags" # read gpr files into a rawData set rawData= read.GenePix(fnames, name.Gf = "F532 Median", name.Gb ="B532 Median", name.Rf = "F635 Median", name.Rb = "B635 Median", name.W ="Flags", layout =galinfo$layout, gnames = galinfo$gnames) #plot array par(ask=T) for (i in 1:length(fnames)){ par(mfrow=c(2,3)) maImage(rawData[,i],x="maGb",bar=F)
maImage(rawData[,i],x="maW", bar=F) } # understand the array layout maImage(rawData[,1],x="maPrintTip") # top 10% differential expression top1 = maTop(maM(rawData[,1]),h=0.1,l=0.1) maImage(rawData[,1],x="maM",subset=top1) par(mfrow=c(2,2)) for (i in 1:3) maImage(rawData[,1],x="maM", maTop(maM(rawData[,i]),h=0.1,l=0.1),bar=F) maRf(rawData)[maW(rawData)<0] <- NA maGf(rawData)[maW(rawData)<0] <- NA maRb(rawData)[maW(rawData)<0] <- NA maGb(rawData)[maW(rawData)<0] <- NA # Normalize - ?maNorm will tell you the options NormData=maNorm(rawData) # Boxplots of Diff Exp # by print-tip on array 1 maBoxplot(rawData[,1],y="maM",x="maPrintTip") maBoxplot(NormData[,1],y="maM",x="maPrintTip") # by array maBoxplot(rawData,y="maM") maBoxplot(NormData,y="maM") # MvA plots (difference in log expression vs mean log expression) for array 1 maPlot(rawData[,1],x="maA",y="maM") maPlot(NormData[,1],x="maA",y="maM") #heatmap (multivariate analysis library) library(mva) RmG=maM(NormData) goodSpot=!is.na(apply(RmG,1,mean)) GoodSpots=RmG[goodSpot,] heatmap(GoodSpots[1:100,], col=maPalette(low="green",high="red",mid="black",k=50)) heatmap(GoodSpots[1:100,], col=maPalette(low="blue",high="red",mid="white",k=50)) heatmap(GoodSpots[1:100,], col=maPalette(low="blue",high="red",mid="white",k=50)) |