I am working with a set of differentially expressed genes in different conditions and in order to understand the results I did various overrepresentation analyses (against Reactome, KEGG and GO terms) and I was wondering if there is a way to "merge" the results all together into unique processes to reduce the redundancy that comes from overlapping processes.
Any idea/tool will be much appreciated!
I'm not sure, but this made me think that if you merge the tables, before you filter using the adjusted p-values to select pathways, because you're going to compare these tests and because the multiple testing adjustment was probably done for each table separately (and especially depending on the p-adjust method used), shouldn't you re-adjust for multiple testing the p-values using all the p-values in the new "bigger" table?
Now you mention it, I think you are correct, but mostly because the enrichment scores are also calculated based on the p-values, so the enrichment of each library are likely not comparable with each other. I think it is still plausible to filter based on the false discovery rates of each database (the way you mentioned I think it would be possible by modifying EnrichR source code). You could still rank enrichment scores of each table rather than all at once. It really depends on the number of significant enrichments, but it will greatly reduce the number of terms you are working with.