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Reference-free deconvolution of DNA methylation data and mediation by cell composition effects
BACKGROUND: Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types. RESULTS: We propose a simple method for reference-free deconvolution that provides both propo...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928286/ https://www.ncbi.nlm.nih.gov/pubmed/27358049 http://dx.doi.org/10.1186/s12859-016-1140-4 |
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author | Houseman, E. Andres Kile, Molly L. Christiani, David C. Ince, Tan A. Kelsey, Karl T. Marsit, Carmen J. |
author_facet | Houseman, E. Andres Kile, Molly L. Christiani, David C. Ince, Tan A. Kelsey, Karl T. Marsit, Carmen J. |
author_sort | Houseman, E. Andres |
collection | PubMed |
description | BACKGROUND: Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types. RESULTS: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying methylomes, the number of these constituent cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent cell types. CONCLUSIONS: Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by cell composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1140-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4928286 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49282862016-06-30 Reference-free deconvolution of DNA methylation data and mediation by cell composition effects Houseman, E. Andres Kile, Molly L. Christiani, David C. Ince, Tan A. Kelsey, Karl T. Marsit, Carmen J. BMC Bioinformatics Methodology Article BACKGROUND: Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types. RESULTS: We propose a simple method for reference-free deconvolution that provides both proportions of putative cell types defined by their underlying methylomes, the number of these constituent cell types, as well as a method for evaluating the extent to which the underlying methylomes reflect specific types of cells. We demonstrate these methods in an analysis of 23 Infinium data sets from 13 distinct data collection efforts; these empirical evaluations show that our algorithm can reasonably estimate the number of constituent types, return cell proportion estimates that demonstrate anticipated associations with underlying phenotypic data; and methylomes that reflect the underlying biology of constituent cell types. CONCLUSIONS: Our methodology permits an explicit quantitation of the mediation of phenotypic associations with DNA methylation by cell composition effects. Although more work is needed to investigate functional information related to estimated methylomes, our proposed method provides a novel and useful foundation for conducting DNA methylation studies on heterogeneous tissues lacking reference data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1140-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-29 /pmc/articles/PMC4928286/ /pubmed/27358049 http://dx.doi.org/10.1186/s12859-016-1140-4 Text en © The Author(s). 2016 Open AccessThis 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. |
spellingShingle | Methodology Article Houseman, E. Andres Kile, Molly L. Christiani, David C. Ince, Tan A. Kelsey, Karl T. Marsit, Carmen J. Reference-free deconvolution of DNA methylation data and mediation by cell composition effects |
title | Reference-free deconvolution of DNA methylation data and mediation by cell composition effects |
title_full | Reference-free deconvolution of DNA methylation data and mediation by cell composition effects |
title_fullStr | Reference-free deconvolution of DNA methylation data and mediation by cell composition effects |
title_full_unstemmed | Reference-free deconvolution of DNA methylation data and mediation by cell composition effects |
title_short | Reference-free deconvolution of DNA methylation data and mediation by cell composition effects |
title_sort | reference-free deconvolution of dna methylation data and mediation by cell composition effects |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4928286/ https://www.ncbi.nlm.nih.gov/pubmed/27358049 http://dx.doi.org/10.1186/s12859-016-1140-4 |
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