方法对比

	单细胞时间序列RNA测序分析	通路富集分析
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2014-2018 (pseudotime and RNA velocity frameworks)	2003–2005
提出者	Trapnell et al. (pseudotime/Monocle); La Manno et al. (RNA velocity)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
类型	Computational bioinformatics pipeline	Statistical functional annotation method
开创性文献	Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.	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	scRNA-seq time course analysis, longitudinal scRNA-seq, temporal single-cell transcriptomics, dynamic single-cell gene expression analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
相关	6	6
摘要	Time-series single-cell RNA-seq analysis captures gene expression across multiple time points at single-cell resolution to reveal how cell populations emerge, transition, and diverge during dynamic biological processes such as development, differentiation, or disease progression. By combining pseudotime ordering, RNA velocity, and differential dynamics testing, researchers reconstruct the temporal trajectory of individual cells and identify the gene regulatory changes that drive biological transitions.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.