Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.
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.
Cell Syst. 2020 May 20;10(5):445-452.e6. doi: 10.1016/j.cels.2020.03.005.
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.
全基因组单细胞 DNA 测序 (scDNA-seq) 能够在细胞水平上对拷贝数图谱进行描述。我们提出了 SCOPE，这是一种针对 scDNA-seq 数据噪声的归一化和拷贝数估计方法。SCOPE 的主要特点包括以下几点：(1) 用于归一化的泊松潜在因子模型，该模型通过使用计算机识别的阴性对照细胞在细胞和区域之间借用信息来估计偏差；(2) 归一化步骤中嵌入的期望最大化算法，该算法考虑了异常的拷贝数变化，并允许直接进行倍性估计，而无需事后调整；(3) 跨样本分割过程，用于识别具有相同遗传背景的细胞之间共享的断点。我们在癌症基因组学中的一系列多样化的 scDNA-seq 数据上评估了 SCOPE，并表明 SCOPE 提供了准确的拷贝数估计，并成功地重建了亚克隆结构。本论文的透明同行评审过程记录包含在补充信息中。
