Whenever I'm interested in comparing groups, I usually specify the design for my DESeq2
/limma
pipeline to include only the variables related to the contrast of interest.
Should I include in the design other variables that I'm not interested in contrasting, but to "correct" the model for side effects (kinda the same you do with multivariate survival models)?
Examples could be: RIN scores for my samples or other sources of technical variation, other grouping variables that may be causally related to my comparison of interest.
Michael Love Please, do you have any suggestions regarding how to deal with the RIN scores. Do you remove low RIN scores or SVA seems to be efficient even with low RIN scores?
Above I indicate that I prefer SVA / RUV to just using RIN if you are keeping the sample. We use a variety of measures in the lab to detect failed samples.