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

Authors

Yushu Liu
Mohammadamin Edrisi
Zhi Yan
Huw A Ogilvie
Luay Nakhleh

Publication Date

4-29-2024

Journal

Algorithms for Molecular Biology

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. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD’s performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD.

Keywords

Copy number aberrations, Single-cell DNA sequencing data, Birth-death model, Phylogenetic inference

DOI

10.1186/s13015-024-00264-4

PMID

38685065

PMCID

PMC11059640

PubMedCentral® Posted Date

4-29-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes