SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing.
Rujin Wang, Dan-Yu Lin, Yuchao Jiang
Author Information
Rujin Wang: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
Dan-Yu Lin: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.
Yuchao Jiang: Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA. Electronic address: yuchaoj@email.unc.edu.
Whole-genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy-number profiles at the cellular level. We propose SCOPE, a normalization and copy-number estimation method for the noisy scDNA-seq data. SCOPE's main features include the following: (1) a Poisson latent factor model for normalization, which borrows information across cells and regions to estimate bias, using in silico identified negative control cells; (2) an expectation-maximization algorithm embedded in the normalization step, which accounts for the aberrant copy-number changes and allows direct ploidy estimation without the need for post hoc adjustment; and (3) a cross-sample segmentation procedure to identify breakpoints that are shared across cells with the same genetic background. We evaluate SCOPE on a diverse set of scDNA-seq data in cancer genomics and show that SCOPE offers accurate copy-number estimates and successfully reconstructs subclonal structure. A record of this paper's transparent peer review process is included in the Supplemental Information.
