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单细胞拷贝数变异分析×Single-cell RNA-seq analysis×
领域生物信息学生物信息学
方法族Process / pipelineProcess / pipeline
起源年份2011–20152009 (first scRNA-seq by Tang et al.); widely adopted 2015–2016
提出者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)
类型Computational genomics pipelineHigh-throughput single-cell transcriptomic profiling 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. link ↗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. DOI ↗
别名scCNV analysis, single-cell CNV, scCNA analysis, single-cell copy number aberration analysisscRNA-seq, single-cell transcriptomics, scRNAseq analysis, single-cell gene expression profiling
相关65
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.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.
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ScholarGate方法对比: Single-cell Copy Number Variation Analysis · Single-cell RNA-seq analysis. 于 2026-06-18 检索自 https://scholargate.app/zh/compare