Hi there, I am trying to analyze two channel Agilent 4x44k microarray by Limma in R. So far I used Limma guide to analyze my data. Unfortunately, I don't have any DE after analysis. Here is my code:
targets<-readTargets("targets.txt")
RG <- read.maimages(targets, path="directory",source="agilent.median")'
RG <- backgroundCorrect(RG, method="normexp", offset=10)
MA <- normalizeWithinArrays(RG, method="loess")
Adjusted p value at the beginning was much more higher than 0.05. So I decided to use this command for cutting of my spike in probes:
MA2<-MA[MA$genes$ControlType==0,]
Then, I came back to standard code:
MA.avg <- avereps(MA2, ID=MA2$genes$ProbeName)
design <- modelMatrix(targets, ref="cycle")
fit <- lmFit(MA.avg, design)
fit2 <- eBayes(fit)
output <- topTable(fit2, adjust="fdr", coef="Pregnancy", number=100000)
write.table(output, file="Results.txt", sep="\t", quote=FALSE)
Still I don't have any DE genes. The least adjusted p value=0.3264. I found out that there is one more option. I can cut off low expressed genes from my list of 45k genes just after normalization step. Here is the code which let me to do that considering sd:
sds<-apply(MA, 1,sd)
cutsd<-quantile(sds, 0.95)
ind<-sds>cutsd
MA.sd<-MA[ind,]
Is it a good one? What does the quantile, 1 and 0.95 value mean? I shouldn't change it, right? If anyone could help me...
I know that there is another option- to do more arrays. But firstly I want to check all possibilities with these which I've done so far. Thanks, a lot! ;)
