I am working on using GSVA to look at differential expression of gene set enrichment scores across several sample groups and the dataset I'm using is from NanoString nCounter (which can be treated like RNA-seq count data). I have read that it is recommended to normalize the data with voomWithQualityWeights prior to using linear modeling with limma. However, since I am planning to perform GSVA and then limma for differential expression of the scores, would it still be appropriate to use voomWithQualityWeights?
My next question is about using lmfit with GSVA enrichment scores. In the documentation and many examples I have seen online, I haven't seen use of the block argument or estimation of the correlation value for differential gene set analysis. Is it not recommended to utilize the block or correlation argument in lmfit at the set level?