方法对比

	基于网络的单细胞RNA测序分析	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	2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017)	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
提出者	Aibar et al. (SCENIC, gene regulatory networks); Jin et al. (CellChat, cell-cell communication networks)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
类型	Computational bioinformatics pipeline	Functional genomics / enrichment analysis
开创性文献	Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., ... & Aerts, S. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086.	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 network analysis, single-cell gene regulatory network inference, scGRN analysis, single-cell co-expression network analysis	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
相关	6	5
摘要	Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, this approach captures the coordinated activity of gene circuits and intercellular signalling pathways within and between cell populations, enabling a systems-level view of transcriptional regulation at single-cell resolution.	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.