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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 <> ## 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]( ## Vignettes [HTML](