I am using DESeq2 for RNAseq analysis. I have 3 conditions (Control, Treatment 1, and Treatment 2). I gave the design matrix formula as
~ replicate + condition. And I get two sets of results as
res1 <- results(dds, contrast=c('condition','control','t1')) res2 <- results(dds, contrast=c('condition','control','t2'))
Now, I am interested in genes that are only regulated in treatment 1 but not in treatment 2. I simply selected genes with
- Is there a systematic way to do this? One way would be to rename treatment 2 also as control. But then, we are only selecting genes DE in treatment 1 compared to the combined condition of control and treatment 2. This doesn't really solve the same problem. Does it? Is there a different way to write a fancy contrast condition to do this?
If we simply use the (adjusted) p-values of the two results obtained separately, what would be the way to formulate the test and arrive at a significance level? If I use
res2$padj>0.05what is the effective significance of this test? Does it depend on how many genes are found significant in treatment 1 and 2 respectively?
Any other ideas? A different package that can handle this?
I can eliminate trying to select for the null by modifying the way I frame the test (and slightly modifying the problem), either by using the results with altHypothesis="lessAbs" or by testing DE between treatments 1 and 2, and selecting genes that show say, signficant but opposite DE from Control to Treatment 1, and 1 to 2. So now, I have two tests that share a condition (or samples), and I want to select genes that are significant in both tests. But these tests are correlated (I think).