SCOPE

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Single-cell COPy number Estimation

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

## Authors
Rujin Wang, Danyu Lin, and Yuchao Jiang

## Maintainer
Rujin Wang <rujinwang1124@gmail.com>

## Installation
From Bioconductor
```r
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. ***Cell Systems***, 2020. ([link](https://www.sciencedirect.com/science/article/abs/pii/S2405471220301113?via%3Dihub))

## Vignettes
[HTML](http://bioconductor.org/packages/devel/bioc/vignettes/SCOPE/inst/doc/SCOPE_vignette.html)

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