TY - JOUR T1 - SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models JF - bioRxiv DO - 10.1101/091595 SP - 091595 AU - Hamim Zafar AU - Anthony Tzen AU - Nicholas Navin AU - Ken Chen AU - Luay Nakhleh Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/12/04/091595.abstract N2 - 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. ER -