| Chapter title |
Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach
|
|---|---|
| Chapter number | 262 |
| Book title |
Population Epigenetics
|
| Published in |
Methods in molecular biology, July 2015
|
| DOI | 10.1007/7651_2015_262 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6901-2, 978-1-4939-6903-6
|
| Authors |
Meaghan J. Jones, Sumaiya A. Islam, Rachel D. Edgar, Michael S. Kobor, Jones, Meaghan J., Islam, Sumaiya A., Edgar, Rachel D., Kobor, Michael S. |
| Abstract |
Analysis of DNA methylation in a population context has the potential to uncover novel gene and environment interactions as well as markers of health and disease. In order to find such associations it is important to control for factors which may mask or alter DNA methylation signatures. Since tissue of origin and coinciding cell type composition are major contributors to DNA methylation patterns, and can easily confound important findings, it is vital to adjust DNA methylation data for such differences across individuals. Here we describe the use of a regression method to adjust for cell type composition in DNA methylation data. We specifically discuss what information is required to adjust for cell type composition and then provide detailed instructions on how to perform cell type adjustment on high dimensional DNA methylation data. This method has been applied mainly to Illumina 450K data, but can also be adapted to pyrosequencing or genome-wide bisulfite sequencing data. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 26% |
| Student > Ph. D. Student | 10 | 26% |
| Student > Bachelor | 7 | 18% |
| Student > Postgraduate | 2 | 5% |
| Other | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 44% |
| Biochemistry, Genetics and Molecular Biology | 9 | 23% |
| Medicine and Dentistry | 3 | 8% |
| Social Sciences | 2 | 5% |
| Nursing and Health Professions | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 4 | 10% |