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| Analiza wzbogacenia zestawów genów (GSEA)× | Sieciowa analiza wzbogacenia zestawów genów× | |
|---|---|---|
| Dziedzina | Bioinformatyka | Bioinformatyka |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) | 2010 (NetGSA); field consolidated 2010-2015 |
| Twórca≠ | 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) |
| Typ≠ | Functional genomics / enrichment analysis | Network-informed statistical enrichment test |
| Źródło pierwotne≠ | 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. DOI ↗ | Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22. link ↗ |
| Inne nazwy | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment | network GSEA, network-propagation GSEA, NetGSA, graph-informed gene set testing |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | 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. |
| ScholarGateZbiór danych ↗ |
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