Võrdle meetodeid

	Diferentsiaalne rajajärjestuse rikastumise analüüs	Teede rikastumise analüüs
Valdkond	Bioinformaatika	Bioinformaatika
Perekond	Process / pipeline	Process / pipeline
Tekkeaasta	2004–2012	2003–2005
Looja	Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tüüp	Comparative enrichment analysis	Statistical functional annotation method
Algallikas	Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.	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.
Rööpnimetused	differential enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Seotud	5	6
Kokkuvõte	Differential pathway enrichment analysis identifies biological pathways whose enrichment signals differ significantly between two or more experimental conditions — for example, between two diseases, two treatments, or two cell types. Rather than asking which pathways are enriched in one condition, it asks which pathways show a statistically meaningful change in enrichment level across conditions, revealing condition-specific or context-dependent biology.	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.