방법 비교

	네트워크 기반 RNA-seq 차등 발현 분석	RNA-seq 차등 발현
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2002–2005	2008–2010 (RNA-seq DE methodology established)
창시자	Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
유형	Integrative computational pipeline	Quantitative genomics pipeline
원전	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.	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.
별칭	network-aware DE analysis, gene network differential expression, co-expression network DE, NB-DEA	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
관련	5	6
요약	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.	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.