rujinwang/SCOPE: A normalization and copy number estimation method for single-cell DNA sequencing

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.

Getting started

Package details

AuthorRujin Wang, Danyu Lin, Yuchao Jiang
Bioconductor views Alignment CopyNumberVariation Coverage DNASeq DataImport Normalization QualityControl Sequencing SingleCell WholeGenome
MaintainerRujin Wang <rujin@email.unc.edu>
LicenseGPL-2
Version0.99.13
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("rujinwang/SCOPE")
rujinwang/SCOPE documentation built on Sept. 10, 2020, 9:19 p.m.