Porovnat metody

	Analýza multi-omických metabolomických dat	Analýza obohacení drah
Obor	Bioinformatika	Bioinformatika
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2000s–2010s (metabolomics ~2000; multi-omics integration ~2010s)	2003–2005
Tvůrce	Pioneered collectively; key early integrative frameworks by Nicholson & Lindon (metabolomics) and Hasin, Seldin & Lusis (multi-omics disease mapping)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Typ	Integrative computational pipeline	Statistical functional annotation method
Původní zdroj	Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14, 1177932219899051.	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.
Další názvy	metabolomics multi-omics integration, integrated metabolomics, multi-omics metabolite profiling, metabolome-centric multi-omics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Příbuzné	5	6
Shrnutí	Multi-omics metabolomics analysis integrates metabolite profiling data — derived from mass spectrometry or NMR spectroscopy — with genomic, transcriptomic, and/or proteomic datasets to build a system-level view of biological phenotypes. By anchoring integration on the metabolome, which reflects the downstream functional output of gene expression and protein activity, this approach connects upstream molecular variation to observable biochemical states, enabling richer mechanistic insight than any single omics layer 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.