方法对比

	单细胞RNA测序差异表达分析	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	2013–2015 (first scRNA-seq DE tools; refined 2015–present)	2003–2005
提出者	Pioneered through Seurat (Satija lab) and scde (Kharchenko lab) frameworks, building on bulk RNA-seq DE foundations	Mootha et al. (2003); systematised by Subramanian et al. (2005)
类型	Computational bioinformatics pipeline	Statistical functional annotation method
开创性文献	Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420.	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 DE, single-cell differential expression, scDE, cell-level differential expression analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
相关	5	6
摘要	Single-cell RNA-seq differential expression (scRNA-seq DE) analysis identifies genes whose expression levels differ significantly between defined groups of individual cells — such as cell types, disease states, or treatment conditions. Unlike bulk RNA-seq, which averages signals across millions of cells, scRNA-seq DE operates on the transcriptome of each individual cell, enabling fine-grained characterization of cell-population-specific gene regulation and heterogeneity within seemingly homogeneous tissue.	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.