Comparer des méthodes

	Analyse d'enrichissement différentiel de voies	Analyse d'enrichissement de jeux de gènes (GSEA)
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2004–2012	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Auteur d'origine	Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Type	Comparative enrichment analysis	Functional genomics / enrichment analysis
Source fondatrice	Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.	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.
Alias	differential enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Apparentées	5	5
Résumé	Differential pathway enrichment analysis identifies biological pathways whose enrichment signals differ significantly between two or more experimental conditions — for example, between two diseases, two treatments, or two cell types. Rather than asking which pathways are enriched in one condition, it asks which pathways show a statistically meaningful change in enrichment level across conditions, revealing condition-specific or context-dependent biology.	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.