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

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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.

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Zafar, H., Tzen, A., Navin, N., Chen, K., & Nakhleh, L. (2017). SiFit: Inferring tumor trees from single-cell sequencing data under finite-sites models. Genome Biology, 18(1). https://doi.org/10.1186/s13059-017-1311-2

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