Hi,
I have an experiment whereby Treatment A and Treatment B both cause a small difference in phenotype, but Treatment A + Treatment B together causes a large difference in phenotype, above what is seen by either alone.
We have RNA-seq data from brain samples of saline treatment (control), treatment A, treatment B and treatment A+B, with 4-5 replicates each. I'm interested in seeing what differences in expression are due to the combination of A+B.
I've used DESeq2 to do comparative analyses between each pairwise comparison,and used two approaches to try and get at the genes deferentially expressed in A+B.
Firstly I looked at differential expression of A vs A+B, and then removed genes found to be significantly different in saline Vs B, to try and look for genes deferentially expressed only in A+B.
The second approach is to look at A vs A+B and B vs A+B and to investigate the genes found to be significant in both.
I think these approaches works, but I may miss things (e.g. a small expression difference in A or B alone, but a much large difference in the combination).
Do the described methods make sense? Is there a better/more elegant way of doing this?
I hope this makes sense, and thank you in advance,
dear David,
If after reading the DESeq2 interactions documentation, you have further questions on the interpretation of coefficients in an interaction model, I'd recommend you discuss with a local statistician at your institute.
It's not so much a software question as a question of how these models work, and these are standard tools in statistics and linear modeling. Beyond writing up documentation and making figures as we have in the vignette section, the best thing to do is to sit in front of a whiteboard with someone who can sketch how these work.