Comparer des méthodes

	Analyse d'enrichissement de jeux de gènes (GSEA)	Analyse d'enrichissement de jeux de gènes basée sur les réseaux
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)	2010 (NetGSA); field consolidated 2010-2015
Auteur d'origine	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)	Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015)
Type	Functional genomics / enrichment analysis	Network-informed statistical enrichment test
Source fondatrice	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.	Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22.
Alias	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment	network GSEA, network-propagation GSEA, NetGSA, graph-informed gene set testing
Apparentées	5	5
Résumé	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.	Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression signals across network edges, allowing genes that are co-regulated or functionally connected to jointly support the significance of a gene set. The result is a biologically coherent enrichment score that accounts for pathway topology and gene-gene dependencies.