方法对比

	单细胞全基因组关联分析 (Single-cell GWAS)	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	2019–2022 (rapid emergence with large-scale scRNA-seq atlases)	2003–2005
提出者	Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022	Mootha et al. (2003); systematised by Subramanian et al. (2005)
类型	Integrative genomic analysis pipeline	Statistical functional annotation method
开创性文献	Zhang, M. J., Hou, K., Dey, K. K., Sakaue, S., Jagadeesh, K. A., Weinand, K., ... & Price, A. L. (2022). Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data. Nature Genetics, 54(8), 1224-1234.	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	sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
相关	6	6
摘要	Single-cell GWAS is an integrative bioinformatics pipeline that maps genome-wide association study (GWAS) signals onto single-cell transcriptomic landscapes to identify which cell types and individual cells carry disproportionate genetic risk for a disease or trait. By leveraging single-cell RNA-seq atlases alongside GWAS summary statistics, it moves beyond tissue-level associations to reveal the precise cellular contexts in which disease-associated genetic variants exert their effects.	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.