Title |
BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference
|
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Published in |
Genome Biology, September 2018
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DOI | 10.1186/s13059-018-1513-2 |
Pubmed ID | |
Authors |
Elior Rahmani, Regev Schweiger, Liat Shenhav, Theodora Wingert, Ira Hofer, Eilon Gabel, Eleazar Eskin, Eran Halperin |
Abstract |
We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 5 | 22% |
Australia | 3 | 13% |
United Kingdom | 2 | 9% |
Canada | 1 | 4% |
Qatar | 1 | 4% |
Germany | 1 | 4% |
Israel | 1 | 4% |
Italy | 1 | 4% |
Unknown | 8 | 35% |
Demographic breakdown
Type | Count | As % |
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Scientists | 11 | 48% |
Members of the public | 8 | 35% |
Science communicators (journalists, bloggers, editors) | 2 | 9% |
Practitioners (doctors, other healthcare professionals) | 2 | 9% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 66 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 18% |
Researcher | 12 | 18% |
Student > Master | 6 | 9% |
Professor | 3 | 5% |
Other | 3 | 5% |
Other | 7 | 11% |
Unknown | 23 | 35% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 16 | 24% |
Agricultural and Biological Sciences | 10 | 15% |
Computer Science | 7 | 11% |
Business, Management and Accounting | 1 | 2% |
Nursing and Health Professions | 1 | 2% |
Other | 5 | 8% |
Unknown | 26 | 39% |