See https://bioconductor.org/packages/release/workflows/vignettes/RnaSeqGeneEdgeRQL/inst/doc/edgeRQL.html for an example workflow, which includes ranking of genes and pathway analyses by GO, KEGG and by focused gene sets.
Unless you have an overwhelming amount of DE, we recommend ranking by FDR and not by fold-change at all.
In my opinion, the use of logFC=1 cutoffs in the literature is arbitrary and counter-productive because it interacts badly with modern empirical Bayes differential expression tests as implemented in limma, edgeR or DESeq2. The 2-fold cutoff comes from the original microarray papers pre-2000 that had no replicates and no statistical tests. Later on, people started to include replicates and ordinary t-tests. It can be shown that ordinary t-tests or Welch t-tests can be improved by adding a logFC cutoff, and that a moderate logFC cutoff can decrease the FDR. This is because a t-test can be highly significant even for very small logFC if the standard deviation is also very small, just by chance.
The empirical Bayes tests implemented by limma and edgeR are different however. They take into account logFC, standard deviation and expression level all together when evaluating significance. They already achieve a compromise (optimal in a certain sense) between ranking by logFC and ranking by t-test. They put on a floor under the posterior variances and do not allow a gene to be significant with an extremely small logFC. So adding a logFC cutoff is no longer necessary and instead becomes counter-productive.
Giving priority to genes with very large logFC but moderate FDR has the effect of prioritizing low expressed genes over highly expressed genes, which tends to select genes that are of less rather than more biological importance.
Another problem is that the Benjamini-Hochberg algorithm for evaluating FDR is based on tail-probabilities for the ranked gene list. If you filter genes from the list after computing the FDR then the whole FDR calculation is invalidated for genes originally further down in the list. It is easy to show that you can actually increase the overall FDR, sometimes substantially, by removing genes with lower logFC values from the list.
Another consideration that highlights the arbitrariness of the logFC cutoff is the fact that logFCs output from limma, edgeR and DESeq2 are not raw log-fold-changes but are rather shrunk to a greater or lesser degree. In edgeR, the amount of shrinkage is user-specified. Which genes will satisfy a logFC=1 cutoff depends on how much shrinkage has been done, but unfortunately people in the literature just seem to apply the logFC=1 cutoff blindly without considering what the estimated logFCs actually mean.
