Comparer des méthodes

	Analyse des variations du nombre de copies unicellulaires	GWAS à cellule unique
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2011–2015	2019–2022 (rapid emergence with large-scale scRNA-seq atlases)
Auteur d'origine	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)	Multiple groups (Price lab, De Jager lab, others); scDRS framework by Zhang et al. 2022
Type	Computational genomics pipeline	Integrative genomic analysis pipeline
Source fondatrice	Garvin, T., Aboukhalil, R., Kendall, J., Baslan, T., Atwal, G. S., Hicks, J., Wigler, M., & Schatz, M. C. (2015). Interactive analysis and assessment of single-cell copy-number variations. Nature Methods, 12(11), 1058–1060.	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.
Alias	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis	sc-GWAS, single-cell GWAS integration, cell-type-specific GWAS, single-cell genetic association analysis
Apparentées	6	6
Résumé	Single-cell copy number variation (scCNV) analysis detects gains and losses of genomic segments within individual cells, enabling researchers to resolve intratumor heterogeneity, reconstruct clonal evolution, and distinguish malignant from normal cells at single-cell resolution. It can be applied to single-cell whole-genome sequencing data directly or inferred from read-depth signals in scRNA-seq or scATAC-seq experiments.	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.