Dear community,

I am analyzing RNAseq data set with DESeq2. Samples are grouped in three groups: one control group (n=16) and two case groups (case 1 and 2, n=6 and n=8, respectively).

I want to find differentially expressed genes of both case groups against the control group, but also against each other (case1 vs case2).

When fitting a GLM, I can easily get the coefficients for case1_vs_control and case2_vs_control, and to compare case1 against case2, I could do case1_vs_control *vs* case2_vs_control, and that would produce differentially expressed genes whose log2FC over the control group differ between case1 and case2.

However, because of the degrees of freedom, I know a different result will come out if I directly compare case1 and case2, ignoring the control group. This also would require fitting another GLM for a two group comparison.

My question is which approach is more robust to compare case1 and case2.

Thanks in advance!

The recommended way in DESeq2 would be to run DESeq() with a design ~condition, where condition has levels "control", "case1", and "case2", and then to use the results() function to build the tables:

res1 <- results(dds, contrast=c("condition","case1","control"))
res2 <- results(dds, contrast=c("condition","case2","control"))
res3 <- results(dds, contrast=c("condition","case2","case1"))