Empirical bayes model comparisons for differential methylation analysis

Mingxiang Teng, Yadong Wang, Seongho Kim, Lang Li, Changyu Shen, Guohua Wang, Yunlong Liu, Tim H.M. Huang, Kenneth P. Nephew, Curt Balch

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number376706
JournalComparative and Functional Genomics
Volume2012
DOIs
StatePublished - 2012

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Biology
  • Genetics

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