Porovnat metody

	Multi-omics Gene Set Enrichment Analysis	Vícevrstvá analýza obohacení drah
Obor	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2005 (GSEA foundation); multi-omics extensions ~2013–2020	2014–2016 (multi-omics extension of enrichment methods established ~2005)
Tvůrce	Extended from Subramanian et al. (2005); multi-omics integration formalized ~2010s	Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)
Typ	Integrative enrichment analysis pipeline	Integrative pathway analysis pipeline
Původní zdroj	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.	Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162.
Další názvy	multi-omics GSEA, integrated GSEA, cross-omics pathway enrichment, multi-layer GSEA	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA
Příbuzné	6	1
Shrnutí	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.	Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss.