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

Copy number alterations (CNA) are important drivers and markers of clonal structures within tumors. Understanding these structures at single-cell resolution is crucial to advancing cancer treatments. The objective is to cluster single cells into clones and identify CNA events in each clone. Early attempts often sacrifice the intrinsic link between cell clustering and clonal CNA detection for simplicity and rely heavily on human input for critical parameters such as the number of clones. Here, we develop a Bayesian model to utilize single-cell RNA sequencing (scRNA-seq) data for automatic analysis of intra-tumoral clonal structure concerning CNAs, without reliance on prior knowledge. The model clusters cells into sub-tumoral clones, identifies the number of clones, and simultaneously infers the clonal CNA profiles. It synergistically incorporates input from gene expression and germline single-nucleotide polymorphisms. A Gibbs sampling algorithm has been implemented and is available as an R package Chloris. We demonstrate that our new method compares strongly against existing software tools in terms of both cell clustering and CNA profile identification accuracy. Application to human metastatic melanoma and anaplastic thyroid 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.