HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher’s exact test

Sun Shuying () and Yu Xiaoqing
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Sun Shuying: Department of Mathematics, Texas State University, San Marcos, TX 78666, USA
Yu Xiaoqing: Department of Biostatistics, Yale University, New Haven, CT 06511, USA

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 1, 55-67

Abstract: DNA methylation is an epigenetic event that plays an important role in regulating gene expression. It is important to study DNA methylation, especially differential methylation patterns between two groups of samples (e.g. patients vs. normal individuals). With next generation sequencing technologies, it is now possible to identify differential methylation patterns by considering methylation at the single CG site level in an entire genome. However, it is challenging to analyze large and complex NGS data. In order to address this difficult question, we have developed a new statistical method using a hidden Markov model and Fisher’s exact test (HMM-Fisher) to identify differentially methylated cytosines and regions. We first use a hidden Markov chain to model the methylation signals to infer the methylation state as Not methylated (N), Partly methylated (P), and Fully methylated (F) for each individual sample. We then use Fisher’s exact test to identify differentially methylated CG sites. We show the HMM-Fisher method and compare it with commonly cited methods using both simulated data and real sequencing data. The results show that HMM-Fisher outperforms the current available methods to which we have compared. HMM-Fisher is efficient and robust in identifying heterogeneous DM regions.

Keywords: bisulfite sequencing; differential methylation; Fisher’s exact test; hidden Markov model (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1515/sagmb-2015-0076

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