Single-cell transcriptomic analysis is widely used to study human tumors. However, it remains challenging to distinguish normal cell types in the tumor microenvironment from malignant cells and to resolve clonal substructure within the tumor. To address these challenges, we developed an integrative Bayesian segmentation approach called copy number karyotyping of aneuploid tumors (CopyKAT) to estimate genomic copy number profiles at an average genomic resolution of 5 Mb from read depth in high-throughput single-cell RNA sequencing (scRNA-seq) data. We applied CopyKAT to analyze 46,501 single cells from 21 tumors, including triple-negative breast cancer, pancreatic ductal adenocarcinoma, anaplastic thyroid cancer, invasive ductal carcinoma and glioblastoma, to accurately (98%) distinguish cancer cells from normal cell types. In three breast tumors, CopyKAT resolved clonal subpopulations that differed in the expression of cancer genes, such as KRAS, and signatures, including epithelial-to-mesenchymal transition, DNA repair, apoptosis and hypoxia. These data show that CopyKAT can aid in the analysis of scRNA-seq data in a variety of solid human tumors.

单细胞转录组学分析广泛用于研究人类肿瘤。然而，将肿瘤微环境中的正常细胞类型与恶性细胞区分开来并解决肿瘤内的克隆亚结构仍然具有挑战性。为了应对这些挑战，我们开发了一种称为非整倍体肿瘤拷贝数核型分析 (CopyKAT) 的综合贝叶斯分割方法，以从高通量单细胞 RNA 测序 (scRNA) 中的读取深度估计平均基因组分辨率为 5 Mb 的基因组拷贝数概况-seq) 数据。我们应用 CopyKAT 分析了来自 21 种肿瘤的 46,501 个单细胞，包括三阴性乳腺癌、胰腺导管腺癌、间变性甲状腺癌、浸润性导管癌和胶质母细胞瘤，以准确 (98%) 区分癌细胞与正常细胞类型。在三个乳腺肿瘤中，KRAS和特征，包括上皮-间充质转化、DNA 修复、细胞凋亡和缺氧。这些数据表明 CopyKAT 可以帮助分析各种实体人类肿瘤中的 scRNA-seq 数据。