Vertaile menetelmiä

	Multi-omics Gene Set Enrichment Analysis	Polkurikastusanalyysi
Tieteenala	Bioinformatiikka	Bioinformatiikka
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2005 (GSEA foundation); multi-omics extensions ~2013–2020	2003–2005
Kehittäjä	Extended from Subramanian et al. (2005); multi-omics integration formalized ~2010s	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tyyppi	Integrative enrichment analysis pipeline	Statistical functional annotation method
Alkuperäislähde	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.
Rinnakkaisnimet	multi-omics GSEA, integrated GSEA, cross-omics pathway enrichment, multi-layer GSEA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Liittyvät	6	6
Tiivistelmä	Multi-omics gene set enrichment analysis (multi-omics GSEA) is a computational pipeline that applies GSEA logic simultaneously across two or more molecular measurement layers — such as transcriptomics, proteomics, and metabolomics — to identify biological pathways or gene sets that are coordinately dysregulated across omics platforms. By integrating ranked molecular signatures from each layer, it reveals pathway-level convergence that no single omics platform could detect alone.	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.