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CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets
Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of varia...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624895/ https://www.ncbi.nlm.nih.gov/pubmed/31300005 http://dx.doi.org/10.1186/s13059-019-1743-y |
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author | Thompson, Mike Chen, Zeyuan Johnson Rahmani, Elior Halperin, Eran |
author_facet | Thompson, Mike Chen, Zeyuan Johnson Rahmani, Elior Halperin, Eran |
author_sort | Thompson, Mike |
collection | PubMed |
description | Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1743-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6624895 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66248952019-07-23 CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets Thompson, Mike Chen, Zeyuan Johnson Rahmani, Elior Halperin, Eran Genome Biol Method Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1743-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-12 /pmc/articles/PMC6624895/ /pubmed/31300005 http://dx.doi.org/10.1186/s13059-019-1743-y Text en © The Author(s) 2019 Open Access 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. |
spellingShingle | Method Thompson, Mike Chen, Zeyuan Johnson Rahmani, Elior Halperin, Eran CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
title | CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
title_full | CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
title_fullStr | CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
title_full_unstemmed | CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
title_short | CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
title_sort | confined: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624895/ https://www.ncbi.nlm.nih.gov/pubmed/31300005 http://dx.doi.org/10.1186/s13059-019-1743-y |
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