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SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models.

ABSTRACT: Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

SUBMITTER: Zafar H

PROVIDER: S-EPMC5606061 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models.

Zafar Hamim H Tzen Anthony A Navin Nicholas N Chen Ken K Nakhleh Luay L

Genome biology 20170919 1

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under ...[more]

PMID: 28927434

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