Best method/package for Gene Set Enrichment Analysis in R?

Question

[Juan Cordero]

Dear community,

I recently started working with GSEA of microarray Data in Bioconductor and after a quick search I am quite overwhelmed because the wide supply of different packages to compute GSEA for a given list of differentially expressed probe sets / genes (goseq, topGO, gega, gsea, GOstats...). In many cases, every method claims to be the best, and it's getting hard for me to choose a proper method.

Do you know if there's any benchmark or rather which method is the best one? and for the analysis of KEGG pathways enrichment?

[informatics bot]

I personally use gage, because it's super easy to make your own custom gene-sets and ranked lists for GSEA. It's also really easy to use it in conjunction with pathview, which is a nice R package for pathway visualization.

[Juan Cordero]

Hi Lando,

I have a table of DE genes with their respective P- and Q-values calculated with LIMMA package and I would like to start from this data to make the GSEA. I've been reading the gega vignette and it seems it must start from the beginning, with the expression matrix. Do you know whether there's another way to make the GSEA starting from a list of genes with their p-values but using gega?

Answer 1

[bigmawen]

One thing needs to be pointed our first, the package is called gage (method called GAGE) instead of gega.

Gage/pathview can take outputs from all major RNA-Seq/expression analysis tool, including limma, deseq2/deseq, edger or cufflinks. You should use log2 fold change or their test statistics columns (if available). P-values would also work (although not preferred) when you set rank.test=T when calling gage.

Please check the joint workflow sections in the RNA-Seq pathway analysis tutorial: http://bioconductor.org/packages/release/bioc/vignettes/gage/inst/doc/RNA-seqWorkflow.pdf