Salīdzināt metodes

	Daudzomu šķērsviršķu bagātības analīze	GSEA (Gēnu kopu bagātināšanas analīze)
Nozare	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2014–2016 (multi-omics extension of enrichment methods established ~2005)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Autors	Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Tips	Integrative pathway analysis pipeline	Functional genomics / enrichment analysis
Pirmavots	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.	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.
Citi nosaukumi	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Saistītās	1	5
Kopsavilkums	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.	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.