CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

Autor: Alexey Kozlov, Joao M. Alves, Alexandros Stamatakis, David Posada
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Genome Biology, Vol 23, Iss 1, Pp 1-30 (2022)
Druh dokumentu: article
ISSN: 1474-760X
DOI: 10.1186/s13059-021-02583-w
Popis: Abstract We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available at https://github.com/amkozlov/cellphy .
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