Hi everyone,

I'm currently analyzing a RNA-Seq data using DESeq2. Looking at the FAQ's in https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html, concerning the subset of data, the authors recommend "users to run samples from all groups together, and then use the contrast argument of the results function to extract comparisons of interest after fitting the model using DESeq."

However, they show an example of when to subset: when we have one group that has a within-group variable higher than the remaining groups. Thus, I would like to know if we can use vegan::betadisper, a function to assess homogeneity of variances within-groups to determine if I should proceed or not with subsetting.

Thanks in advance!

That's an interesting idea, and you could try it. I don't have any specific advice on whether that test is appropriate, but it looks like if you provided a distance matrix as we calculate in the workflow it would work.

I typically just look at PCA plots to determine if one group is very different in terms of its spread. And usually it's good to know this for all kinds of downstream applications and for biological interpretation.