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

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 .

SUBMITTER: Kozlov A

PROVIDER: S-EPMC8790911 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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

Kozlov Alexey A Alves Joao M JM Stamatakis Alexandros A Posada David D

Genome biology 20220126 1

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 s ...[more]

PMID: 35081992

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