Порівняння методів

	Мережевий аналіз збагачення генних наборів	Аналіз диференційної експресії генів методом RNA-seq
Галузь	Біоінформатика	Біоінформатика
Родина	Process / pipeline	Process / pipeline
Рік появи	2010 (NetGSA); field consolidated 2010-2015	2008–2010 (RNA-seq DE methodology established)
Автор методу	Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
Тип	Network-informed statistical enrichment test	Quantitative genomics pipeline
Основоположне джерело	Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22.	Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
Інші назви	network GSEA, network-propagation GSEA, NetGSA, graph-informed gene set testing	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
Пов'язані	5	6
Підсумок	Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression signals across network edges, allowing genes that are co-regulated or functionally connected to jointly support the significance of a gene set. The result is a biologically coherent enrichment score that accounts for pathway topology and gene-gene dependencies.	RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.