I have been using EdgeR from the Bioconductor package to perform differential expression analysis on bulk RNASeq data sets.
This analysis gives me an output dataframe that has a list of all the genes, ranked by the their adjP values. This dataframe also contains the t-statistic and lfc values for each gene as well.
I have recently been using enrichR to perform gene set enrichment analysis on all the differentially expressed genes to determine what biological, molecular, transcriptional, etc pathways these genes are involved in. I really like the enrichR package for a few key reasons:
1.) It has access to many different databases that I can call on with a simple vector of their names. Most of my data is murine RNASEq, so for those analyses, I can simply select the murine databases:
enrichR_dbs <- c("WikiPathways_2019_Mouse", "KEGG_2019_Mouse", "GO_Biological_Process_2018", "GO_Cellular_Component_2018", "GO_Molecular_Function_2018", "GWAS_Catalog_2019", "TRRUST_Transcription_Factors_2019")
2.) It uses a single function, enrichR, to perform the enrichment. The arguments for that function are the list of gene symbols and the list of databases.
enrichr(as.character(Gene_Dataframe$SYMBOL[1:500]), enrichR_dbs)
Anyway, I am looking for another enrichment analysis because I want to be able to see if I get similar pathways for my genes when using another method/algorithm.
Unfortunately, I can't seem to find another enrichment analysis( competitive or self-contained) that is both as user-friendly and that gives me easy access to murine databases (without requiring to convert my genes through changes in capitalization, like you need to with GSEA) as enrichR.
Does anyone have recommendations about enrichment analysis R packages that don't require too many more inputs than the dataframe I generated through my EdgeR analysis?
P.S. I'm ok with downloading databases if I need to. Though inclusion of a PDF with a tutorial would be fantastic, if possible.
