方法对比

	时间序列通路富集分析	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	2005–2014	2008–2010 (RNA-seq DE methodology established)
提出者	Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
类型	Functional enrichment analysis with temporal modeling	Quantitative genomics pipeline
开创性文献	Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168.	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	temporal pathway analysis, longitudinal pathway enrichment, dynamic pathway analysis, TPEA	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
相关	5	6
摘要	Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses.	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.