방법 비교

	단일 세포 복제수 변이 분석	변이 호출
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2011–2015	2009–2010 (modern high-throughput era)
창시자	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
유형	Computational genomics pipeline	Computational genomics pipeline
원전	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.	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.
별칭	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis	SNP calling, genotyping from sequencing, mutation detection, variant detection
관련	6	6
요약	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.	Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.