Comparer des méthodes

	Analyse d'enrichissement de jeux de gènes (GSEA)	Analyse de l'expression différentielle par RNA-seq
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)	2008–2010 (RNA-seq DE methodology established)
Auteur d'origine	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Type	Functional genomics / enrichment analysis	Quantitative genomics pipeline
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.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
Alias	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Apparentées	5	6
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.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.