方法对比

	网络表观基因组关联研究 (Network EWAS)	RNA-seq差异表达
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2010s, consolidating 2012–2018	2008–2010 (RNA-seq DE methodology established)
提出者	Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型	Integrative epigenomic analysis	Quantitative genomics pipeline
开创性文献	Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.	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.
别名	network EWAS, network-integrated EWAS, graph-based EWAS, network-based DNA methylation analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关	6	6
摘要	Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways.	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.