I am interested to selecte the top genes that carry signal in the dataset. What is the difference between the most variable genes across samples from these two functions?
library("genefilter") topVarGenes <- head(order(-rowVars(assay(vsd))),30) #Different from the top 30 from here (some are, some aren't): DEG <- subset(res, padj <0.1)
- Is this because res have the results from the DESeq2 without the VST
- Would it make any sense to obtain the DEG after vst to obtain the most variable genes after normalisation? (this is what topVarGenes does)
- If they are different, which one are to use for what?