library(limma) library(affy) library(geneplotter) setwd("GLDilution") pd <- read.phenoData("pdata.txt",as.is=TRUE) sns <- paste("L","S_",pd$LiverAmt,"_",pd$SN19Amt,sep="") fns <- paste(pd$filename,"gz",sep=".") Data<-ReadAffy(filenames=fns[11:15],phenoData=pd[1:5,],description="miame.txt") sampleNames(Data) <- LETTERS[1:5] bitmap("../figure-01.png",res=300,pointsize=20) mypar(1,1) boxplot(Data) dev.off() bitmap("../figure-02.png",res=300,pointsize=20) mypar(1,1) hist(Data) dev.off() ###Affy SpikeIn library(SpikeIn) data(SpikeIn95) for(i in 1:2){ if(i==1) Data <- SpikeIn95[,9:10] else Data <- normalize(Data) x <- log2(pm(Data[,1])) y <- log2(pm(Data[,2])) if(i==1){ bitmap("../figure-03-0.png",res=300,pointsize=20) mypar(1,1) smoothScatter(x,y,xlab="Expression 1",ylab="Expression 2") dev.off() } A <- (x+y)/2 M <- y-x Index <- which(abs(M)<1.2) A <- A[Index] M <- M[Index] bitmap(paste("../figure-03-",i,".png",sep=""),res=300,pointsize=20) mypar(1,1) smoothScatter(A,M) sIndex <- which(probeNames(Data)[Index]=="40322_at") points(A[sIndex],M[sIndex],col="red",pch=16,cex=.25) fit1 <- loess(M~A,subset=order(A)[seq(1,length(A),len=2000)], degree=1,span=1/3) if(i==1) simfit <- fit1 ###for next plot lines(sort(fit1$x),fit1$fitted[order(fit1$x)],col=2) dev.off() } ##Loess demo A <- seq(min(simfit$x),max(simfit$x),len=2000) M <- predict(simfit,A) + rnorm(length(A),0,0.15) bitmap("../figure-04.png",res=300,pointsize=20) mypar(1,1) plot(A,M,cex=.25,col=3) fit1 <- loess(M~A,degree=1,span=1/3) lines(sort(fit1$x),fit1$fitted[order(fit1$x)],col=2) dev.off() for(i in 1:5){ bitmap(paste("../figure-04-",i,".png",sep=""),res=300,pointsize=20) mypar(1,1) plot(A,M,cex=.25,col=3) fit1 <- loess(M~A,degree=1,span=1/3) cutoff <- (8:12)[i] a <- A[which(A>cutoff-1 & Acutoff-1 & A -75) ##RG <- read.maimages(targets$FileName, source="genepix", wt.fun=f) ##RG$printer <- getLayout(RG$genes) ##RG$printer