Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Thompson, Mike, Chen, Zeyuan Johnson, Rahmani, Elior, Halperin, Eran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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
_version_ 1783434304943030272
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
work_keys_str_mv AT thompsonmike confineddistinguishingbiologicalfromtechnicalsourcesofvariationbyleveragingmultiplemethylationdatasets
AT chenzeyuanjohnson confineddistinguishingbiologicalfromtechnicalsourcesofvariationbyleveragingmultiplemethylationdatasets
AT rahmanielior confineddistinguishingbiologicalfromtechnicalsourcesofvariationbyleveragingmultiplemethylationdatasets
AT halperineran confineddistinguishingbiologicalfromtechnicalsourcesofvariationbyleveragingmultiplemethylationdatasets