4 conditions in DESeq2

[mattanswers]

I have four conditions A, B, C, and D that each have three HTSeq count files.

I want to use DESeq2 to compare A to B, A to C, A to D, B to C, B to D etc.

With DESeq, I would bring all the 12 files into a countDataSet and then I could specify which of the conditions to compare.

With DESeq2, I can bring all the 12 files into a DESeqDataSet, but I can not figure out how to do specify the particular comparisons.

[Dario1984]

It's easy. Use the contrast variable of the results function. Comparing all pairs of contrasts is not a good idea, though. Why don't you have a better biological hypothesis ?

[mattanswers]

We have wild type condition A and wild type condition B as well as treated condition A and treated condition B. So, we would like to see changes in gene expression between condition A and condition B, as well as changes in gene expression between wild type and treated.

Will contrast provide us with this ? To me it seems like the analysis is done by the DESeq function and the results function just organizes the output of the DESeq function.

[dpryan]

Yes, a contrast allows you to perform pretty much any comparison you want. Note that the comparisons to A don't require contrasts, since those are just fit coefficients.

[Michael Love]

hi Matt,

For lots more information about what you want to do, check out the man page for ?results and section 3.2 Contrasts in: vignette("DESeq2")

[mattanswers]

Thank you very much for your answers Dario, Devon and Michael !

[Michael Love]

> "Note that the comparisons to A don't require contrasts, since those are just fit coefficients."

Safest always to use contrast=c("condition","B","A"), because when we have expanded model matrices for symmetric shrinkage, then the coefficients are not B_vs_A, but simply A, B, etc. This will be clear from resultsNames(dds)

[mattanswers]

OK. Thank you for clarifying that.
(And also for making DESeq2 !)