เปรียบเทียบวิธี

	การศึกษาความสัมพันธ์ทางพันธุกรรมทั่วทั้งจีโนมในระดับเซลล์เดี่ยว	การวิเคราะห์ความอุดมสมบูรณ์ของวิถีชีวภาพ
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2019–2022 (rapid emergence with large-scale scRNA-seq atlases)	2003–2005
ผู้ริเริ่ม	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)
ประเภท	Integrative genomic analysis pipeline	Statistical functional annotation method
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
ที่เกี่ยวข้อง	6	6
สรุป	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.