Empirical Bayes model comparisons for differential methylation analysis

Mingxiang Teng, Yadong Wang, Yunlong Liu, Seongho Kim, Curt Balch, Kenneth P. Nephew, Lang Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A number of empirical Bayes models were developed to investigate the differential methylation analysis. However, it is not clear which empirical Bayes model performs best in differential methylation analysis. In this paper, five empirical Bayes models were constructed and applied to the differential methylation aualysis of A2780 cells between before and after 1, 3, and 5 round of cisplatin treatment. The log-normal model with the background variance showed the lowest minimized negative log-likelihood. It inferred increasing number of differentially methylated loci from 1 to 3 to 5 rounds of cisplatin treatment on the A2780 cells, which was consistent to cisplatin resistant IC50 data. Among differentially methylated loci selected from each empirical model, three time dependent methylation patterns were defined: stochastic hypomethylation, stochastic hypermethylation, and random methylation. If the empirical Bayes model of the DNA methylation assumed log-normal distribution, both stochastically hypomethylated loci and stochastically hypermethylated loci were enriched with a number of transcription factor binding sites. Almost no TFBS enrichment was observed if the gamma distribution was assumed in the empirical Bayes model. In summary, by comparing the performances of the differential methylation analysis and the TFBS enrichment analysis, log-normal distribution is a better statistical assumption than the gamma distribution in the empirical Bayes model.

Original languageEnglish (US)
Title of host publicationProceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
PublisherIEEE Computer Society
Pages9-12
Number of pages4
ISBN (Print)9781467304900
DOIs
StatePublished - 2011
Event2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 - San Antonio, TX, United States
Duration: Dec 4 2011Dec 6 2011

Publication series

NameProceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
ISSN (Print)2150-3001
ISSN (Electronic)2150-301X

Other

Other2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11
CountryUnited States
CitySan Antonio, TX
Period12/4/1112/6/11

Keywords

  • Differential methylation analysis
  • Empirical Bayes model
  • Transcription factor binding site enrichment

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Computational Theory and Mathematics
  • Signal Processing
  • Biomedical Engineering

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  • Cite this

    Teng, M., Wang, Y., Liu, Y., Kim, S., Balch, C., Nephew, K. P., & Li, L. (2011). Empirical Bayes model comparisons for differential methylation analysis. In Proceedings 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11 (pp. 9-12). [6169428] (Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics). IEEE Computer Society. https://doi.org/10.1109/gensips.2011.6169428