CONFINED: Distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

Mike Thompson; Zeyuan Johnson Chen; Elior Rahmani; Eran Halperin

doi:10.1186/s13059-019-1743-y

CONFINED: Distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

Mike Thompson, Zeyuan Johnson Chen, Elior Rahmani, Eran Halperin^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

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.

Original language	English
Article number	138
Journal	Genome Biology
Volume	20
Issue number	1
DOIs	https://doi.org/10.1186/s13059-019-1743-y
State	Published - 12 Jul 2019
Externally published	Yes

Funding

Funders	Funder number
Israel Science Foundation585	1425/13
National Science Foundation	1705197
National Institutes of Health
National Institute of Mental Health	R01MH115979
Edmond J. Safra Center for Ethics, Harvard University

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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.",

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AB - 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.

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CONFINED: Distinguishing biological from technical sources of variation by leveraging multiple methylation datasets

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