Abstract Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via “bulk sequencing”, the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. In this paper, we introduce a new birth-death evolutionary model of CNAs as well as a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data under this model. We assessed the accuracy of our method on both simulated and biological data and compared it to the accuracy of two standard phylogenetic tools, namely neighbor-joining and maximum parsimony (MP). We show through simulations that our method infers more accurate topologies and branch lengths. We also studied the ancestral state reconstruction accuracy with the birth-death evolutionary model and found it outperformed MP. Finally, running all three methods on a colorectal cancer data set, we observed that among all three methods, only the phylogeny inferred by NestedBD clearly separated the primary tumor cells from the metastatic ones, providing a more plausible history of the tumor cells.

NestedBD: Bayesian Inference of Phylogenetic Trees From Single-Cell DNA Copy Number Profile Data Under a Birth-Death Model