Hi Michael,

I have a dataset of five different conditions with three repeats each and my aim is to conduct a few different pairwise comparison between the different conditions. I followed successfully your instructions from Wed Jun 11 15:32:11 about how to do it in DESeq 2.
However, while analyzing the results I saw some strange numbers where big differences did not get significant Padj- when I repeated the analysis with uploading only the relevant conditions the Padj dropped dramatically.

One example:

*        pairwise comparison from the big data set - normalized reads of condition a: 216,241; 204,221; 229,866. condition b: 11,192; 10,840; 9,172 Padj: 0.132

*        comparison in a dataset of the relevant conditions only: normalized reads of condition a: 220,126; 205,579; 230,317. condition b: 11,341; 10,897; 9,233 Padj: 1.74E-215

Can you think of what might have caused this problem?
I'm wondering if I should go back and analyze each comparison separately.

The basic script I have used:

sampleFiles <- c("P11.txt","P12.txt","P13.txt","P21.txt","P22.txt","P23.txt","L11.txt","L12.txt","L13.txt","L21.txt","L22.txt","L23.txt","L31.txt","L32.txt","L33.txt")
sampleCondition<-c("a","a","a","e","e","e","ILa","ILa","ILa","ILe","ILe","ILe","ILh","ILh","ILh")
sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles, condition=sampleCondition)
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)
colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition, levels=c("a","e","ILa","ILe","ILh"))
dds<-DESeq(ddsHTSeq)
res<-results(dds, contrast=c("condition","a","ILa"))
res<-res[order(res$padj),]
mcols(res,use.names=TRUE)
write.csv(as.data.frame(res),file="a-ILa.csv")

All the best,

Avichai


	[[alternative HTML version deleted]]

