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commented
5 years ago Hi @rujinwang
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The DESCRIPTION file for this package is:
Package: SCOPE
Type: Package
Title: A normalization and copy number estimation method for single-cell DNA sequencing
Version: 0.99.0
Author: Rujin Wang, Danyu Lin, Yuchaojiang
Maintainer: Rujin Wang <rujin@email.unc.edu>
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.
Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb
Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics
Suggests:
knitr,
rmarkdown,
WGSmapp,
testthat (>= 2.1.0)
VignetteBuilder: knitr
biocViews: SingleCell,
Normalization,
CopyNumberVariation,
Sequencing, WholeGenome,
Coverage,
Alignment,
QualityControl,
DataImport
License: GPL-2
LazyData: true
RoxygenNote: 6.1.1
Encoding: UTF-8
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hpages
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