Módszerek összehasonlítása

	Multi-omikai proteomikai analízis	Génkészlet-gazdagítási elemzés (GSEA)
Tudományterület	Bioinformatika	Bioinformatika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2010s (integrative multi-omics frameworks emerged ~2012–2019)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Megalkotó	Le Cao, K.-A. and colleagues (mixOmics/DIABLO framework); broader field rooted in Aebersold & Mann proteomics work	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Típus	Integrative computational pipeline	Functional genomics / enrichment analysis
Alapmű	Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752.	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.
Alternatív nevek	integrative proteomics, multi-omics proteomics integration, proteogenomics multi-omics, cross-omics proteomics	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Kapcsolódó	6	5
Összefoglaló	Multi-omics proteomics analysis integrates protein abundance data from mass spectrometry with at least one additional omics layer — such as genomics, transcriptomics, or metabolomics — to build a systems-level view of biological regulation. Rather than analyzing proteins in isolation, this approach correlates proteomic profiles with upstream molecular events (e.g., DNA variants, mRNA levels) and downstream functional readouts (e.g., metabolite concentrations), enabling discovery of regulatory drivers 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.