Methoden vergleichen

	Differential Pathway Enrichment Analysis	Netzwerkbasierte Pathway-Anreicherungsanalyse
Fachgebiet	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2004–2012	2002 (seminal network-scoring concept); matured 2010–2015
Urheber	Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)	Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others
Typ	Comparative enrichment analysis	Pathway enrichment and network analysis method
Wegweisende Quelle	Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.	Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(suppl_1), S233–S240.
Aliasnamen	differential enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis	network pathway enrichment, network-based enrichment, topology-based pathway analysis, NBPEA
Verwandt	5	1
Zusammenfassung	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.	Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enrichment approaches that treat pathway genes as independent lists, this family of methods propagates signals across network edges, capturing the topology of interactions and uncovering dysregulated modules that flat-list enrichment would miss.