Title |
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
|
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Published in |
Genome Biology, September 2017
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DOI | 10.1186/s13059-017-1311-2 |
Pubmed ID | |
Authors |
Hamim Zafar, Anthony Tzen, Nicholas Navin, Ken Chen, Luay Nakhleh |
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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Geographical breakdown
Country | Count | As % |
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United States | 12 | 32% |
United Kingdom | 5 | 14% |
France | 2 | 5% |
Austria | 1 | 3% |
China | 1 | 3% |
India | 1 | 3% |
Belgium | 1 | 3% |
Canada | 1 | 3% |
Unknown | 13 | 35% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 20 | 54% |
Members of the public | 14 | 38% |
Science communicators (journalists, bloggers, editors) | 3 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | <1% |
Unknown | 152 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 42 | 27% |
Researcher | 30 | 20% |
Student > Bachelor | 13 | 8% |
Student > Master | 11 | 7% |
Student > Doctoral Student | 7 | 5% |
Other | 16 | 10% |
Unknown | 34 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 38 | 25% |
Biochemistry, Genetics and Molecular Biology | 36 | 24% |
Computer Science | 26 | 17% |
Mathematics | 5 | 3% |
Engineering | 2 | 1% |
Other | 8 | 5% |
Unknown | 38 | 25% |