Accurate genome-scale percentage DNA methylation estimates from microarray data (original) (raw)

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Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University and Department of Biostatistics, Johns Hopkins Bloomberg, School of Public Health, Baltimore, MD 21231, USA, aryee@jhu.edu

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Center for Statistical Sciences, Brown University, Providence, RI 02912, USA

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Center for Epigenetics and Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, MD 21231, USA

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Center for Epigenetics and Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, MD 21231, USA

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Center for Epigenetics and Department of Medicine, Johns Hopkins University, School of Medicine, Baltimore, MD 21231, USA

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Srinivasan Yegnasubramanian

Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21231, USA

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Department of Biostatistics, Johns Hopkins Bloomberg, School of Public Health, Baltimore, MD 21231, USA

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Received:

17 February 2010

Revision received:

27 July 2010

Published:

21 September 2010

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Martin J. Aryee, Zhijin Wu, Christine Ladd-Acosta, Brian Herb, Andrew P. Feinberg, Srinivasan Yegnasubramanian, Rafael A. Irizarry, Accurate genome-scale percentage DNA methylation estimates from microarray data, Biostatistics, Volume 12, Issue 2, April 2011, Pages 197–210, https://doi.org/10.1093/biostatistics/kxq055
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Abstract

DNA methylation is a key regulator of gene function in a multitude of both normal and abnormal biological processes, but tools to elucidate its roles on a genome-wide scale are still in their infancy. Methylation sensitive restriction enzymes and microarrays provide a potential high-throughput, low-cost platform to allow methylation profiling. However, accurate absolute methylation estimates have been elusive due to systematic errors and unwanted variability. Previous microarray preprocessing procedures, mostly developed for expression arrays, fail to adequately normalize methylation-related data since they rely on key assumptions that are violated in the case of DNA methylation. We develop a normalization strategy tailored to DNA methylation data and an empirical Bayes percentage methylation estimator that together yield accurate absolute methylation estimates that can be compared across samples. We illustrate the method on data generated to detect methylation differences between tissues and between normal and tumor colon samples.

© The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

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