I have a question regarding how robust DESeq2 is to large class imbalances for differential gene expression. I am currently analyzing RNA-seq data from the GTEx database, and I have gone through a workflow of identifying whether certain tissue samples are "hot" or "cold" in terms of immune infiltration. This workflow yields a (somewhat expected) gross class imbalance between "hot" and "cold" samples, with the latter outweighing the former for some tissues by a couple orders of magnitude (ex. 233 cold vs 6 hot in Adipose tissue). I've found some of Michael Love's commentary on Bioconductor to be very helpful regarding how well DESeq2 can handle both low sample sizes and large class imbalances:
If I have interpreted his comments correctly, it seems that DESeq2 is quite robust in these situations, and classes that are very imbalanced or containing as few as 2-3 samples are not an issue for DESeq2. However, my question concerns how well DESeq2 performs in the setting of both. Can I perform differential expression when the class imbalance is 151 vs 1? Should I use a minimum number, say 5 samples at least in each class, as a threshold for performing differential expression?
Further, according to Kevin Blighe in this thread (https://www.biostars.org/p/273086/), profound class imbalance can result in a large number of genes passing the FDR q value threshold. Is there a systematic way to choose the FDR in this setting such that I can have confidence in the list of differentially expressed genes?
Thanks in advance for all your help!