Порівняння методів

	Мережевий аналіз диференційної експресії RNA-seq	Аналіз збагачення генних наборів (GSEA)
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2002–2005	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Автор методу	Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Тип	Integrative computational pipeline	Functional genomics / enrichment analysis
Основоположне джерело	Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article 17.	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.
Інші назви	network-aware DE analysis, gene network differential expression, co-expression network DE, NB-DEA	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Пов'язані	5	5
Підсумок	Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meaningful gene modules that change together between conditions. This approach substantially reduces false positives and surfaces pathway-level signals invisible to gene-by-gene testing.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.