DOI: 10.18129/B9.bioc.SCOPE    

This package is for version 3.11 of Bioconductor; for the stable, up-to-date release version, see SCOPE.

A normalization and copy number estimation method for single-cell DNA sequencing

Bioconductor version: 3.11

Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background.

Author: Rujin Wang, Danyu Lin, Yuchao Jiang

Maintainer: Rujin Wang <rujin at>

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biocViews Alignment, CopyNumberVariation, Coverage, DNASeq, DataImport, Normalization, QualityControl, Sequencing, SingleCell, Software, WholeGenome
Version 1.0.0
In Bioconductor since BioC 3.11 (R-4.0) (0.5 years)
License GPL-2
Depends R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19
Imports stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors
Suggests knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0)
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