I am trying to use DESeq2 to compare the effect of a treatment between males and females. I have paired samples, and my colData looks as such:

my design is therefore: ~ sex + sex:nested + sex:condition

However, I've also read that the sample should be treated as a random variable and be included in the design matrix. This would mean that my colData looks like:

and my design would be ~ patient + sex + sex:nested + sex:condition

Is this necessary, or have I been misinterpreting what I'm reading? Should I stick with the first or second design? Any and all help is much appreciated.

Thanks,

Greg

DESeq2 only supports fixed effects not random, so it's really the first design vs using some other approach entirely. Sometimes you hear people say that certain kind of terms must be fit as random vs fixed, but really here what is important is that the differences across samples are modeled, and therefore you perform inference on the fold change of post vs pre within sample. So according to my view, the first design is sufficient.