方法对比

	差异通路富集分析	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	2004–2012	2008–2010 (RNA-seq DE methodology established)
提出者	Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型	Comparative enrichment analysis	Quantitative genomics pipeline
开创性文献	Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133.	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 enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关	5	6
摘要	Differential pathway enrichment analysis identifies biological pathways whose enrichment signals differ significantly between two or more experimental conditions — for example, between two diseases, two treatments, or two cell types. Rather than asking which pathways are enriched in one condition, it asks which pathways show a statistically meaningful change in enrichment level across conditions, revealing condition-specific or context-dependent biology.	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.