PT  - JOURNAL ARTICLE
AU  - Hamim Zafar
AU  - Anthony Tzen
AU  - Nicholas Navin
AU  - Ken Chen
AU  - Luay Nakhleh
TI  - SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models
AID  - 10.1101/091595
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 091595
4099  - http://biorxiv.org/content/early/2016/12/04/091595.short
4100  - http://biorxiv.org/content/early/2016/12/04/091595.full
AB  - Background Tumor phylogenies provide insightful information on intra-tumor heterogeneity and evolutionary trajectories. Single-cell sequencing (SCS) enables the inference of tumor phylogenies and methods were recently introduced for this task under the infinite-sites assumption.Results Violations of this assumption, due to chromosomal deletions and loss of heterozygosity, necessitate the development of statistical inference methods that utilize finite-site models. We propose a statistical inference method for tumor phylogenies from noisy SCS data under a finite-sites model. We demonstrate the performance of our method on synthetic and biological data sets.Conclusion Our results suggest that employing a finite-sites model leads to improved inference of tumor phylogenies.