Uporedite metode

	Analiza diferencijalne obogaćenosti puteva	Analiza obogaćenosti puteva višestrukih omika
Oblast	Bioinformatika	Bioinformatika
Porodica	Process / pipeline	Process / pipeline
Godina nastanka	2004–2012	2014–2016 (multi-omics extension of enrichment methods established ~2005)
Tvorac	Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)	Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016)
Tip	Comparative enrichment analysis	Integrative pathway analysis pipeline
Temeljni izvor	Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.	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.
Drugi nazivi	differential enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis	multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA
Srodne	5	1
Sažetak	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.	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.