I am currently running functional analysis on gene expression data.
I am using gsePathway to identify pathways of interest, to which I can then overlap the differentially regulated genes using viewPathway.
While writing my results, I decided to pull the genes in the core_enrichment column from the object created by gsePathway, and noticed that not all genes (with fold change values) that show up in the Interferon alpha/beta signaling pathway (33) are present in the core_enrichment from gsePathway.
Now, that pathway has a NES of -2.44, and all 21 genes from gsePathway are downregulated in the viewPathway network plot. The remaining genes (33-21 = 12) are either upregulated or have a fold change of 0.
A while ago I checked the GSEA documentation on the Broad website.
Leading edge genes: "As described in the Gene Set Enrichment Analysis PNAS paper, the leading-edge subset in a gene set are those genes that appear in the ranked list at or before the point at which the running sum reaches its maximum deviation from zero. The leading-edge subset can be interpreted as the core that accounts for the gene sets enrichment signal." Source.
Core enrichment genes: "Genes that contribute to the leading-edge subset within the gene set. This is the subset of genes that contributes most to the enrichment result." Source.
The leading edge information is given as percentages (tags, list, signal), and these metrics are used to define the leading edge subset, which are thus the core enrichment genes.... See halfway this page (section "Detailed Enrichment Results").
I agree it is somewhat confusing.
Maybe this (old) clusterProfiler page on visualization is also of interest.
