方法对比

	时间序列基因集富集分析	基因集富集分析 (GSEA)
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2005 (GSEA foundation); time-series adaptations 2007–2014	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
提出者	Extension of GSEA (Subramanian et al., 2005); time-series adaptations developed through maSigPro (Conesa lab) and related tools	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
类型	Gene set enrichment method for longitudinal omics data	Functional genomics / enrichment analysis
开创性文献	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
别名	longitudinal GSEA, dynamic GSEA, time-course GSEA, TS-GSEA	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
相关	6	5
摘要	Time-series gene set enrichment analysis (TS-GSEA) extends the classical GSEA framework to detect biologically coordinated gene sets — pathways, gene ontology terms, or curated signatures — whose collective expression changes meaningfully over time. Rather than comparing two snapshots, it models the full temporal trajectory of gene expression to identify which functional programs are activated, suppressed, or dynamically remodelled during a biological process such as development, treatment response, or disease progression.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.