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Functional normalization of 450k methylation array data improves replication in large cancer studies

Jean-Philippe Fortin, Aurélie Labbe, Mathieu Lemire, Brent W. Zanke, View ORCID ProfileThomas J. Hudson, Elana J. Fertig, Celia M.T. Greenwood, View ORCID ProfileKasper D. Hansen
doi: https://doi.org/10.1101/002956
Jean-Philippe Fortin
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
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Aurélie Labbe
2Department of Epidemiology, Biostatistics and Occupational Health, McGill University
3Douglas Mental Health University Institute, McGill University
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Mathieu Lemire
4Ontario Institute of Cancer Research
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Brent W. Zanke
5Clinical Epidemiology Program, Ottawa Hospital Research Institute
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Thomas J. Hudson
4Ontario Institute of Cancer Research
6Departments of Molecular Genetics and Medical Biophysics, University of Toronto
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  • ORCID record for Thomas J. Hudson
Elana J. Fertig
7Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine
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Celia M.T. Greenwood
2Department of Epidemiology, Biostatistics and Occupational Health, McGill University
8Lady Davis Institute for Medical Research, Jewish General Hospital Montreal
9Department of Oncology, McGill University
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Kasper D. Hansen
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
10McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine
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  • ORCID record for Kasper D. Hansen
  • For correspondence: khansen@jhsph.edu
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Abstract

We propose an extension to quantile normalization which removes unwanted technical variation using control probes. We adapt our algorithm, functional normalization, to the Illumina 450k methylation array and address the open problem of normalizing methylation data with global epigenetic changes, such as human cancers. Using datasets from The Cancer Genome Atlas and a large case-control study, we show that our algorithm outperforms all existing normalization methods with respect to replication of results between experiments, and yields robust results even in the presence of batch effects. Functional normalization can be applied to any microarray platform, provided suitable control probes are available.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 23, 2014.
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Functional normalization of 450k methylation array data improves replication in large cancer studies
Jean-Philippe Fortin, Aurélie Labbe, Mathieu Lemire, Brent W. Zanke, Thomas J. Hudson, Elana J. Fertig, Celia M.T. Greenwood, Kasper D. Hansen
bioRxiv 002956; doi: https://doi.org/10.1101/002956
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Functional normalization of 450k methylation array data improves replication in large cancer studies
Jean-Philippe Fortin, Aurélie Labbe, Mathieu Lemire, Brent W. Zanke, Thomas J. Hudson, Elana J. Fertig, Celia M.T. Greenwood, Kasper D. Hansen
bioRxiv 002956; doi: https://doi.org/10.1101/002956

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