So sánh phương pháp

	GWAS đơn bào	Phân tích làm giàu đường dẫn
Lĩnh vực	Tin sinh học	Tin sinh học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2019–2022 (rapid emergence with large-scale scRNA-seq atlases)	2003–2005
Người khởi xướng	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)
Loại	Integrative genomic analysis pipeline	Statistical functional annotation method
Công trình gốc	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.
Tên gọi khác	sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Liên quan	6	6
Tóm tắt	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.