Hello all -
We've recently been looking at some of the abundant RNASeq data from the NIH's cancer genome atlas to find differentially expressed genes in biological replicates from paired normal/tumour samples, different tumours, etc. We've had success using DESeq2 and SAMSeq on raw count data, and the high sample number has been giving low FDRs/q values.
We're only interested in a small set (~30) of genes of interest vary between conditions. I was wondering, then, what are the statistical pitfalls of excluding other genes before input to DESeq2, SAMSeq etc? I'm aware that these apply normalisation which takes into account reads across all genes. Is there any other part of the DE analysis that might be thrown off by this? On the other hand, is there anything to be said for reducing the number of multiple comparisons being performed? Or is this just generally a bad idea?
(there are some peripheral benefits to excluding the genes, including easier data extraction and less processing time over hundreds of samples, at least in DESeq2).
Thanks in advance! Jon
Hello all -
For DESeq2, the normalization, dispersion estimation and the width of the prior on LFC all are designed for you to provide all the genes (thousands of rows over which to learn parameters).
When it comes to testing, if you are truly setting aside only 30 genes for testing beforehand, and you can write down this list in stone, you could just correct the p-values for these genes, by subsetting and manually using p.adjust(). If you look at the p-values and then change your mind about which genes to test, this kind of an "adaptive" testing regime would lose type I error control.