方法对比

	单细胞拷贝数变异分析	拷贝数变异分析
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2011–2015	1998–2006
提出者	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)	Pinkel et al. (array CGH); Redon et al. (genome-wide CNV map)
类型	Computational genomics pipeline	Genomic structural variant detection pipeline
开创性文献	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.	Redon, R., Ishikawa, S., Fitch, K. R., et al. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444–454.
别名	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis	CNV analysis, copy number variant detection, CNV calling, somatic copy number alteration analysis
相关	6	6
摘要	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.	Copy number variation (CNV) analysis is a genomic pipeline for detecting regions where individuals carry fewer or more copies of a DNA segment than the reference genome. CNVs span kilobases to megabases and are a major class of structural variation implicated in cancer, neurodevelopmental disorders, and population diversity. The pipeline typically processes SNP array intensities or read-depth signals from whole-genome sequencing, applies segmentation algorithms, calls gain and loss events, and annotates them against gene and clinical databases.