Porównaj metody

	Analiza eQTL oparta na sieciach	Analiza wzbogacenia szlaków
Dziedzina	Bioinformatyka	Bioinformatyka
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2008–2013 (network-integrated extensions of eQTL mapping)	2003–2005
Twórca	Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Typ	Statistical genomics / network analysis pipeline	Statistical functional annotation method
Źródło pierwotne	Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J., & Holmes, I. H. (2009). JBrowse: a next-generation genome browser. Genome Research, 19(9), 1630–1638.	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.
Inne nazwy	network eQTL, network-integrated eQTL mapping, graph-based eQTL analysis, eQTL network analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Pokrewne	5	6
Podsumowanie	Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway structures — to improve statistical power, reduce multiple testing burden, and reveal how genetic variants perturb entire regulatory programs rather than isolated transcripts.	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.