方法对比

	差异性表观基因组关联研究	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	2009–2011	2008–2010 (RNA-seq DE methodology established)
提出者	Rakyan, Down, Balding & Beck (2011); Irizarry group for differential methylation methods (~2009–2014)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型	Comparative epigenome-wide 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	Differential EWAS, comparative EWAS, epigenome-wide differential methylation analysis, EWAS differential design	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关	6	6
摘要	A Differential Epigenome-Wide Association Study (Differential EWAS) scans hundreds of thousands of CpG methylation sites across the genome to identify those whose methylation levels differ significantly between two or more comparison groups — such as cases vs. controls, exposed vs. unexposed, or distinct developmental stages. It is the standard epigenomic analogue of a differential expression analysis but operates at the level of DNA methylation marks rather than RNA counts.	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.