So firstly, I'm completely aware that this type of question has been asked multiple times (I know this since I've been scrolling over these type of questions for the past 2 days), but I'm actually more interested in knowing the reasons as to why some people prefer
I've performed differential expression analysis using DESeq2 and I want to see which Gene ontology terms, KEGG pathway terms etc are enriched in my data set. I've initially tried using clusterProfileR, but I keep getting 3 enriched terms for all my differentially expressed genes using
enrichGO(). I also know that some input in clusterProfileR requires you to put logFC values, so I wasn't sure if that was for ALL the genes analysed, or just the differentially expressed genes.
I've also used goseq but my main issue with that is the GO terms are too broad.
I also only have about 300 DEGs, so I'm not really sure if this sort of analysis is best performed when you have a myriad of DEGs, or can be done with a small number.
Anyway, looking forward to hearing people's responses :)