I'm using edgeR to do differentially expressed genes analysis. Here's part of my code:
fit <- glmFit(y, design) # conduct likelihood ratio tests for tumour vs normal tissue differences and show the top genes lrt <- glmLRT(fit) # the DEA result for all the genes dea <- lrt$table # differentially expressed genes toptag <- topTags(lrt, n = length(geneList), p.value = 0.05) deg <- toptag$table
I got a 'PValue' in 'dea', then I'm wondering whether it's a p-value or adjusted p-value. Then I gave 'lrt' to 'topTags' to extract the differentially expressed genes, and set the cutoff of p.value to 0.05, then I'm wondering whether this cutoff is set for the 'PValue' in 'dea'. But then I got a 'FDR' in 'deg' (from 'toptag'), and I found that all the genes are with a FDR < 0.05, but not all the genes in 'dea' with a PValue < 0.05 are listed in 'deg'.
That's what I found from the result. So what I'm thinking now is: the PValue in dea is just a p-value, not adjusted. The function topTags will adjust those p-value with method such as 'BH', and after that, it will provide you with the differentially expressed genes with FDR smaller than the threshold that you set (but somehow it's 'p.value' here, instead of 'FDR' ~~~). So the FDR here is the same as adjusted p-value here. Is my understanding right?