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Comparative and Functional Genomics
Volume 2012 (2012), Article ID 376706, 10 pages
http://dx.doi.org/10.1155/2012/376706
Research Article

Empirical Bayes Model Comparisons for Differential Methylation Analysis

1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
2Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA
3Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
4Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
5Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN 46202, USA
6Department of Molecular Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
7Medical Sciences Program, Indiana University School of Medicine, Bloomington, IN 47405, USA
8Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indianapolis, IN 46202, USA

Received 26 March 2012; Revised 15 June 2012; Accepted 29 June 2012

Academic Editor: G. Pesole

Copyright © 2012 Mingxiang Teng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A number of empirical Bayes models (each with different statistical distribution assumptions) have now been developed to analyze differential DNA methylation using high-density oligonucleotide tiling arrays. However, it remains unclear which model performs best. For example, for analysis of differentially methylated regions for conservative and functional sequence characteristics (e.g., enrichment of transcription factor-binding sites (TFBSs)), the sensitivity of such analyses, using various empirical Bayes models, remains unclear. In this paper, five empirical Bayes models were constructed, based on either a gamma distribution or a log-normal distribution, for the identification of differential methylated loci and their cell division—(1, 3, and 5) and drug-treatment-(cisplatin) dependent methylation patterns. While differential methylation patterns generated by log-normal models were enriched with numerous TFBSs, we observed almost no TFBS-enriched sequences using gamma assumption models. Statistical and biological results suggest log-normal, rather than gamma, empirical Bayes model distribution to be a highly accurate and precise method for differential methylation microarray analysis. In addition, we presented one of the log-normal models for differential methylation analysis and tested its reproducibility by simulation study. We believe this research to be the first extensive comparison of statistical modeling for the analysis of differential DNA methylation, an important biological phenomenon that precisely regulates gene transcription.