My experimental design is the following:

I am interested in obtained D.E. genes for the question HE vs DI given a tissue type. I chose

to merge the tissue_type and genotype columns for the design as recommended by the DESeq2 authors. 

However, the number of samples is un-balanced in my experimental design. That means that, for instance,

tissue type A may have twice as more replicates than tissue type B. This would imply that the question

HE vs DI (for tissue type A) would yield more D.E. genes at a given threshold than for tissue type B.

However, I want to know if a given gene is D.E. in HE vs DI for tissue type A and not for tissue type B and in both too, etc...

So, I am wondering:

- Should I balance the sample sizes by selecting randomly replicates from the tissue type that contains more? 

- Should I introduce an interaction term so my formula would become: ~ tissue_type + genotype + tissue_type:genotype

Thanks for the help!

The unbalanced group size will affect power but I don't think that you should down-sample the larger group. You should just be aware that there is more statistical power (sensitivity) for the groups that have more samples.

The interaction term is only useful here if you want to test for the interaction. If you want to test for DE within each tissue, then your approach is the simplest (this is discussed in the vignette section on interactions, with a diagram).