Gene set enrichment analysis asks whether the genes that changed in an experiment cluster in a known pathway or signature, turning a list of genes into a biological story.
Gene set enrichment analysis identifies classes of genes that are over-represented in a large gene list and may be associated with a phenotype (Wikipedia); Subramanian and colleagues' GSEA ranks all genes by association with a phenotype and tests whether a set concentrates at the top or bottom. Single-sample GSEA, introduced by Barbie and colleagues, scores one sample at a time, producing per-patient pathway activity scores that can be model features. The gene sets come from MSigDB (hallmark, KEGG, Reactome, GO collections). Enrichment describes; it does not prove mechanism.
Shares Pathway activation state (phosphosignalling), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Gene co-expression structure, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathway activation state (phosphosignalling), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathway activation state (phosphosignalling), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathway activation state (phosphosignalling), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Gene co-expression structure, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Pathway activation state (phosphosignalling), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.