CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets
Abstract 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...
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Online Access: | http://link.springer.com/article/10.1186/s13059-019-1743-y |
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doaj-1b388c7e19ea4d8abf70257cd24beae62020-11-25T03:44:42ZengBMCGenome Biology1474-760X2019-07-0120111510.1186/s13059-019-1743-yCONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasetsMike Thompson0Zeyuan Johnson Chen1Elior Rahmani2Eran Halperin3Department of Computer Science, University of California Los AngelesDepartment of Computer Science, University of California Los AngelesDepartment of Computer Science, University of California Los AngelesDepartment of Computer Science, University of California Los AngelesAbstract 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 .http://link.springer.com/article/10.1186/s13059-019-1743-y |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mike Thompson Zeyuan Johnson Chen Elior Rahmani Eran Halperin |
spellingShingle |
Mike Thompson Zeyuan Johnson Chen Elior Rahmani Eran Halperin CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets Genome Biology |
author_facet |
Mike Thompson Zeyuan Johnson Chen Elior Rahmani Eran Halperin |
author_sort |
Mike Thompson |
title |
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_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_sort |
confined: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets |
publisher |
BMC |
series |
Genome Biology |
issn |
1474-760X |
publishDate |
2019-07-01 |
description |
Abstract 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 . |
url |
http://link.springer.com/article/10.1186/s13059-019-1743-y |
work_keys_str_mv |
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