README.md

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

SCOPE

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

Authors

Rujin Wang, Danyu Lin, and Yuchao Jiang

Maintainer

Rujin Wang [e-mail]

Installation

From Bioconductor

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("WGSmapp")
BiocManager::install("SCOPE")

From GitHub

install.packages('devtools')
devtools::install_github("rujinwang/WGSmapp")
devtools::install_github("rujinwang/SCOPE")

Description

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. We evaluate performance of SCOPE on real scDNA-seq data sets from cancer genomic studies. Compared to existing methods, SCOPE more accurately estimates subclonal copy number aberrations and is shown to have higher correlation with array-based copy number profiles of purified bulk samples from the same patient. We further demonstrate SCOPE on three recently released data sets using the 10X Genomics single-cell CNV pipeline and show that it can reliably recover 1% of the cancer cells from a background of normal.

Manuscript

Rujin Wang, Danyu Lin, and Yuchao Jiang. SCOPE: A Normalization and Copy Number Estimation Method for Single-Cell DNA Sequencing. bioRxiv, 2020. (link)

Vignettes

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SCOPE documentation built on Nov. 8, 2020, 5:27 p.m.