Diference between Co-expressed gene-set enrichment analysis (COGENA) and gene-set enrichment analysis?

Question

[omargmc.tak]

Hello everybody, I have a doubt about which of these methods is better to have a gen ontology and pathway analysis. What I understand is that I can do a gen ontology and pathway analysis only having the names of the deregulated genes, but gene-set enrichment analysis takes into account information about foldchange, but I'm not sure what is the difference with Co-expressed gene-set enrichment analysis (COGENA). Which of these methods is better to do a gen ontology and pathway analysis? I will appreciate it if somebody could recommend me some R packages to apply them. Thanks!

Answer 1

[LChart]

COGENA applies a clustering method to the expression data to define gene modules, and these modules are tested for enrichment. I'm not sure precisely its workflow, but you can imagine doing one of the following manually:

1. Subset to DE genes; cluster; perform GO/GSEA on clusters

2. Cluster genes, perform cluster enrichment for DE genes, perform GO/GSEA on enriched clusters

3. Cluster genes, perform cluster DE analysis (eigengene, mean expression, GSVA), perform GO/GSEA on DE clusters

and whatever variations you have. The question of which approach is "best" is ill-defined; and my preference is generally to combine baseline GSEA with GSVA and post-hoc gene clustering for a multi-faceted view of the data.