A Hierarchical Bayesian Approach to Detect Differentially Methylated Loci from Bisulfite Sequencing Data Open Access

Feng, Hao (2013)

Permanent URL: https://etd.library.emory.edu/concern/etds/79407x932?locale=en
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Abstract

DNA methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. In studies of DNA methylation, one key task is to identify methylation differences under distinct biological contexts. Recently, as bisulfite sequencing technology (BS-seq) has made it possible to detect methylation in CG loci level, more and more datasets are becoming available to study DNA methylation. A common drawback of datasets in these studies; however, is that the number of sample replicates is usually limited. This can lead to unstable estimation of within group variance, and may subsequently yield unsatisfactory results in differentially methylated loci (DML) detection. Here we propose a new method to apply shrinkage to the variance estimation in an empirical Bayes model. We show that the variance shrinkage in these data can be done by shrinking a dispersion parameter. Simulation results demonstrate the favorable performance of the new methods.

Table of Contents

Table of Contents

Introduction ............................................... Page 1

Methods ............................................... Page 5

Results ............................................... Page 12

Discussion ............................................... Page 23

Reference ............................................... Page 25

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