Accurate genome-scale percentage DNA methylation estimates from microarray data

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

Research output: Contribution to journalArticle

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

Fingerprint

DNA Methylation
Microarray Data
Methylation
Percentage
Genome
Microarray
Estimate
Normalize
Empirical Bayes
Systematic Error
Profiling
Regulator
High Throughput
Normalization
Preprocessing
Tumor
Enzymes
Restriction
Gene
Estimator

Keywords

  • DNA methylation
  • Epigenetics
  • Microarray

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Accurate genome-scale percentage DNA methylation estimates from microarray data. / Aryee, Martin J.; Wu, Zhijin; Ladd-Acosta, Christine Marie; Herb, Brian; Feinberg, Andrew P; Yegnasubramanian, S; Irizarry, Rafael A.

In: Biostatistics, Vol. 12, No. 2, 04.2011, p. 197-210.

Research output: Contribution to journalArticle

Aryee, Martin J. ; Wu, Zhijin ; Ladd-Acosta, Christine Marie ; Herb, Brian ; Feinberg, Andrew P ; Yegnasubramanian, S ; Irizarry, Rafael A. / Accurate genome-scale percentage DNA methylation estimates from microarray data. In: Biostatistics. 2011 ; Vol. 12, No. 2. pp. 197-210.
@article{a55844fcace3461f953f132511740089,
title = "Accurate genome-scale percentage DNA methylation estimates from microarray data",
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.",
keywords = "DNA methylation, Epigenetics, Microarray",
author = "Aryee, {Martin J.} and Zhijin Wu and Ladd-Acosta, {Christine Marie} and Brian Herb and Feinberg, {Andrew P} and S Yegnasubramanian and Irizarry, {Rafael A.}",
year = "2011",
month = "4",
doi = "10.1093/biostatistics/kxq055",
language = "English (US)",
volume = "12",
pages = "197--210",
journal = "Biostatistics",
issn = "1465-4644",
publisher = "Oxford University Press",
number = "2",

}

TY - JOUR

T1 - Accurate genome-scale percentage DNA methylation estimates from microarray data

AU - Aryee, Martin J.

AU - Wu, Zhijin

AU - Ladd-Acosta, Christine Marie

AU - Herb, Brian

AU - Feinberg, Andrew P

AU - Yegnasubramanian, S

AU - Irizarry, Rafael A.

PY - 2011/4

Y1 - 2011/4

N2 - 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.

AB - 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.

KW - DNA methylation

KW - Epigenetics

KW - Microarray

UR - http://www.scopus.com/inward/record.url?scp=79953152880&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953152880&partnerID=8YFLogxK

U2 - 10.1093/biostatistics/kxq055

DO - 10.1093/biostatistics/kxq055

M3 - Article

C2 - 20858772

AN - SCOPUS:79953152880

VL - 12

SP - 197

EP - 210

JO - Biostatistics

JF - Biostatistics

SN - 1465-4644

IS - 2

ER -