It would be very appreciate if if you could check this question out. Michael Love

Thanks very, very much for your answer.

I would like to start with an A1 vs A0 comparison, then a B1 vs B0 comparison, and finally realize ((A1 vs A0) vs (B1 vs B0)). I designed the comparisons by referring to the book 'RNA-seq Data Analysis: A Practical Approach', where it is mentioned that such complex comparisons can be realized using DESeq2. However it now looks like I don't fully understand how it actually works. In my experimental design, unlike in the book, my groups A and B have their own controls, A0 and B0. The book uses a common control for the complex comparison. So, is it possible that the comparison I want to make cannot be realized using DESeq2 when using different controls?? Or can you help me such as tweaking the code to achieve the comparison I want? Thanks very very much again!

Create the DESeqDataSet object using patient, run, time, and treatment as covariates: dds <- DESeqDataSetFromMatrix(countData = countData, colData = meta, design = ~patient + run + time + treatment)

If you had wanted an overall comparison of the 24 and 48 h time points (including all treatments and patients), you could have used results(dds,'time_48h_vs_24 h') instead, and so on. What if we wanted to a more speciffc or complex comparison? For example, we might want to compare the difference between DPN and control, on the one hand, and the difference between OHT and control, on the other hand, that is, a difference of differences which would tell us something about DPN treatment effects that are not seen in OHT treatment. To do this, we could encode the (DPN vs. control) vs (OHT vs. control) using the contrast argument of the results function (note that this is only available in DESeq2 versions 1.1.24 and above). As the vector returned by resultsNames() has 'treatment_DPN_vs_Control' as its seventh entry and 'treatment_OHT_vs_Control' as its eighth entry, we can do res <- results(dds,contrast=c(0,0,0,0,0,0,1,-1))