I have an RNA-seq dataset that is looking at small RNAs. I am using blockbuster to group my mapped reads into blocks and then I would like to perform a differential expression analysis on the blocks (based on counts of uniquely-mapped reads assigned to each block in each sample). However, I don't know if tools like edgeR and DESeq will work properly for this, because unlike protein-coding genes, these small RNAs might not be predominantly unchanging in their expression levels.
I think I can use an approach similar to this paper in order to select a set of blocks that I am reasonably certain are not differentially expressed between samples. Essentially, I would be selecting a subset of blocks whose expression ranks change very little relative to each other across all the samples. Question 1: Is this a reasonable approach to selecting a set of genes to use as references for differential expression? Question 2: Assuming that I have such a set of genes that I believe to be not differentially expressed (i.e. proverbial "housekeeping" genes), how can I make use of this information in edgeR or DESeq?