Sammenlign metoder

	Genesettanrikelsesanalyse (GSEA)	Pathway Enrichment Analysis
Fagfelt	Bioinformatikk	Bioinformatikk
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)	2003–2005
Opphavsperson	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Type	Functional genomics / enrichment analysis	Statistical functional annotation method
Opprinnelig kilde	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.	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	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relaterte	5	6
Sammendrag	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.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.