Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models

Xinlei Wang, Miao Zang, Guanghua Xiao

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Epigenetics is the study of changes to the genome that can switch genes on or off and determine which proteins are transcribed without altering the DNA sequence. Recently, epigenetic changes have been linked to the development and progression of disease such as psychiatric disorders. High-throughput epigenetic experiments have enabled researchers to measure genome-wide epigenetic profiles and yield data consisting of intensity ratios of immunoprecipitation versus reference samples. The intensity ratios can provide a view of genomic regions where protein binding occur under one experimental condition and further allow us to detect epigenetic alterations through comparison between two different conditions. However, such experiments can be expensive, with only a few replicates available. Moreover, epigenetic data are often spatially correlated with high noise levels. In this paper, we develop a Bayesian hierarchical model, combined with hidden Markov processes with four states for modeling spatial dependence, to detect genomic sites with epigenetic changes from two-sample experiments with paired internal control. One attractive feature of the proposed method is that the four states of the hidden Markov process have well-defined biological meanings and allow us to directly call the change patterns based on the corresponding posterior probabilities. In contrast, none of existing methods can offer this advantage. In addition, the proposed method offers great power in statistical inference by spatial smoothing (via hidden Markov modeling) and information pooling (via hierarchical modeling). Both simulation studies and real data analysis in a cocaine addiction study illustrate the reliability and success of this method.

Original languageEnglish (US)
Pages (from-to)2292-2307
Number of pages16
JournalStatistics in Medicine
Volume32
Issue number13
DOIs
StatePublished - Jun 15 2013

Fingerprint

Change Detection
Epigenomics
Markov Model
Pattern Recognition
Markov Process
Genomics
Genome
Markov Chains
Protein
Experiment
Bayesian Hierarchical Model
Hierarchical Modeling
Spatial Dependence
Pooling
Posterior Probability
Statistical Inference
Progression
Modeling
DNA Sequence
High Throughput

Keywords

  • Bayesian hierarchical modeling
  • Epigenetic alteration
  • Epigenetics
  • Gibbs sampler
  • Hidden Markov model
  • MCMC
  • Posterior samples
  • Spatial dependence
  • Spatial smoothing

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models. / Wang, Xinlei; Zang, Miao; Xiao, Guanghua.

In: Statistics in Medicine, Vol. 32, No. 13, 15.06.2013, p. 2292-2307.

Research output: Contribution to journalArticle

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