방법 비교

	단일 세포 복제수 변이 분석	단일 세포 RNA-seq 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2011–2015	2009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
창시자	Navin et al. (single-cell sequencing for CNV); Garvin et al. (Ginkgo tool, 2015)	Azim Surani, Barbara Treutlein, and the Regev/McCarroll groups (foundational droplet-based methods ~2015)
유형	Computational genomics pipeline	High-throughput single-cell transcriptomic profiling 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.	Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5), 495–502.
별칭	scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysis	scRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
관련	6	5
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.	Single-cell RNA sequencing (scRNA-seq) analysis characterises gene expression at the resolution of individual cells, enabling discovery of cell types, states, and transitions that are invisible in bulk transcriptomics. Starting from raw sequencing reads, the workflow produces a cell-by-gene count matrix and proceeds through quality control, normalisation, dimensionality reduction, unsupervised clustering, cell-type annotation, and a range of downstream analyses such as trajectory inference and differential expression between cell populations.