I am trying to do some differential expression analysis with deseq2 at the moment and I have samples with 3 different groups; genotype (A and B), treatment (control and treated), time (1hr vs 30). Each set of conditions has at least 3 replicates. I have tried to have a read through previous threads and noticed that one of the suggestions is grouping all these factors together which I have done and it provided some useful comparisons, however I would also like to try and do analysis using interaction terms and I had a few questions about this.
One of the main aspects I want to look at it is whether the treatment effect is different over genotype as well as generally looking at genotype differences. I've had a look at ?results and I think I understand how this is done with 2 different groups but wasn't sure with 3, how my design should be?
Would a design of ~ genotype + treatment + time + genotype:treatment be reasonable here?
Considering this design would then passing these arguments to results give me:
contrast=c("genotype","B","A") -- give me the differences due to genotype taking into account any differences due to treatment/time?
contrast=c("treatment","treated","control) -- give me the differences due to treatment overall or only at genotype A?
list(c("treatment_treated_vs_control","genotypeB.treatmenttreated")) -- give me just the differences in the treatment effect between genotype B vs genotype A? or just the total treatment effect for genotype B?
name="genotypeB.treatmenttreated" -- give me the difference in the treatment effect between genotype B vs genotype A.
Sorry, I know a lot of this is covered in the vignette/?results but I just wanted to make sure with 3 factors. I was also wondering how the time aspect would play into these comparisons, and if I should be adding another interaction term to my design (treatment:time) as while the control treatment shouldn't change over time, the treated samples should have a difference between the 2 time points.