Hi,

I have a RNA-seq dataset with the following design and I want to find how Treatment is affected by the Group the samples belong to.

From the vignette, I understand that my design needs to be: ~Group+Treatment+Group+Treatment

and the results are given by

design(dds) <- ~ Group + Treatment + Group:Treatment dds <- DESeq(dds) resultsNames(dds)

the Treatment effect for Group A: res1=results(dds, contrast=c("Treatment", "B", "A"))

the Treatment effect for Group B: res2=results(dds, list( c("Treatment_B_vs_A", "GroupB.TreatmentB") ))

the interaction term, answering: is the Treatment effect different across groups? res3=results(dds, name="GroupB.TreatmentB")

What I wanted to ask is how one can interpret the results in res3 and whether they would be the same if I prepare a Venn diagram from res1 and res2?

Due to limited time for the support site, I have to restrict myself to answering software-related questions. If you have statistical analysis questions, I recommend to work with a local statistician or someone familiar with linear models in R. The terms used by DESeq2 are built off of the basic linear model coefficients in R.