Accurate genome-scale percentage DNA methylation estimates from microarray data

Martin J. Aryee, Zhijin Wu, Christine Ladd-Acosta, Brian Herb, Andrew P. Feinberg, Srinivasan Yegnasubramanian, Rafael A. Irizarry

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)197-210
Number of pages14
JournalBiostatistics
Volume12
Issue number2
DOIs
StatePublished - Apr 2011

Keywords

  • DNA methylation
  • Epigenetics
  • Microarray

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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