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

Genome Biology

卷:18

Nicholas Navin¹  Ken Chen¹  Luay Nakhleh²  Anthony Tzen²  Hamim Zafar² 

[1] Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center;

[2] Department of Computer Science, Rice University;

关键词: Tumor evolution;    Intra-tumor heterogeneity;    Single-cell sequencing;    Finite-sites model;    Phylogenetic tree;   

DOI  :  10.1186/s13059-017-1311-2

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown