**180**wrote:

I have an experiment with two treatments, `A`

and `B`

, with a 2x2 design (four conditions: `∅`

, `A`

, `B`

, and `AB`

, with `∅`

being control), and I have RNA-Seq for each condition (9 biological replicates). I want to do some sort of pathway/gene set analysis to characterize the gene expression changes that separate `A`

from `∅`

but then change in the opposite direction between `AB`

and `A`

. In other words, what can I learn about the effects of `A`

monotherapy that are reversed in the dual therapy `AB`

?

The most obvious thing is to look at the intersection of DEGs in the `A - ∅`

and `A - AB`

and then do hypergeometric/overrepresentation tests against my gene sets. Along the same lines, but a bit more "rigorous" and no harder, I can do GSEA or CAMERA or such-like with both contrasts and then find the intersection of differentially expressed pathways.

However, it would be nice to do this analysis "in one step" using some sort of statistical framework that allows for three conditions in one test. Does any such test exist? I mean, my suspicion is that the answer is no and that linear models in general are not designed to do this kind of thing.

It's relatively simple to describe what I'm looking for as "effects of

`A`

reversed by`B`

", but I am aware that that language is substantively imprecise - literal reversal would be treating with`A`

then`B`

. The biological question of interest would be more accurately described as "effects of`A - ∅`

absent/attenuated in`AB - ∅`

".180