SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
| dc.contributor.author | Zafar, Hamim | en_US |
| dc.contributor.author | Tzen, Anthony | en_US |
| dc.contributor.author | Navin, Nicholas | en_US |
| dc.contributor.author | Chen, Ken | en_US |
| dc.contributor.author | Nakhleh, Luay | en_US |
| dc.date.accessioned | 2017-09-24T04:09:13Z | en_US |
| dc.date.available | 2017-09-24T04:09:13Z | en_US |
| dc.date.issued | 9/19/2017 | en_US |
| dc.date.updated | 2017-09-24T04:09:13Z | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | Zafar, Hamim, Tzen, Anthony, Navin, Nicholas, et al.. "SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models." (2017) BioMed Central: http://dx.doi.org/10.1186/s13059-017-1311-2. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.1186/s13059-017-1311-2 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/97745 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | BioMed Central | en_US |
| dc.rights | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | en_US |
| dc.rights.holder | The Author(s) | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |