Bayesian inference for copy number intra-tumoral heterogeneity from single-cell RNA-sequencing data

High-resolution molecular characterization of intra-tumoral clonal structure defined by genomic and epigenomic alterations is crucial in understanding the natural history of tumors and advancing cancer treatment strategies. Copy number alterations (CNA) are of notable importance as both drivers and markers of clonal structure that can now be assayed at individual cell resolution. However, specific computational methods are needed for accurate inference of clonal profiles and cell states from sparse and noisy single-cell omics data. Here, we develop a new Bayesian model to utilize single-cell RNA sequencing (scRNA-seq) data for automatic analysis of intra-tumoral clonal structure with respect to CNAs, without reliance on prior knowledge. The model clusters cells into sub-tumoral clones while simultaneously identifying CNA events in each clone, jointly modelling input from gene expression and germline single-nucleotide polymorphisms. Unlike previous methods, our approach automatically infers the number of clones present in the tumor. In detailed simulation studies our model frequently achieves very high (>90%) cell clustering accuracy and high (>80%) CN state inference accuracy, even in settings of high variance and sparsity. Overall, our method compares strongly against existing software tools. Application to human metastatic melanoma tumor data demonstrates accurate clustering of tumor and non-tumor cells, and reveals clonal CNA profiles that highlight functional gene expression differences between clones from the same tumor. Our method is implemented in a publicly-available, open-source R package, Chloris.