SCClone: Accurate Clustering of Tumor Single-Cell DNA Sequencing Data - Yu , Zhenhua; Du, Fang; Song, Lijuan (Frontiers in Genetics, 2022)
Single-cell DNA sequencing (scDNA-seq) enables high-resolution profiling of genetic diversity among single cells, and is especially useful to decipher the intra-tumor heterogeneity and evolutionary history of tumor. Specific technical issues such as allele dropout, false positive errors and doublets make scDNA-seq data incomplete and error-prone, giving rise to a severe challenge of accurately inferring clonal architecture of tumor. To effectively address these issues, we introduce a new computational method called SCClone for reasoning subclones from single nucleotide variation (SNV) data of single cells. Specifically, SCClone leverages a probability mixture model for binary data to cluster single cells into distinct subclones. To accurately decipher underlying clonal composition, a novel model selection scheme based on inter-cluster variance is employed to find the optimal number of subclones. Extensive evaluations on various simulated datasets suggest SCClone has strong robustness against different technical noises in scDNA-seq data, and achieves better performance than the state-of-the-art methods in reasoning clonal composition. Further evaluations of SCClone on three real scDNA-seq datasets show it can effectively find the underlying subclones from severely disturbed data. The SCClone software is freely available at https://github.com/qasimyu/scclone.