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SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
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SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
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Journal Article
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
2017
Overview
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.
Publisher
BioMed Central,Springer Nature B.V,BMC
