MethylSeqDesign: a framework for Methyl-Seq genome-wide power calculation and study design issues

Author:

Liu Peng1,Lin Chien-Wei2,Park Yongseok1,Tseng George1

Affiliation:

1. Department of Biostatistics, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261, USA

2. Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA

Abstract

Summary Bisulfite DNA methylation sequencing (Methyl-Seq) becomes one of the most important technologies to study methylation level difference at a genome-wide scale. Due to the complexity and large scale of methyl-Seq data, power calculation and study design method have not been developed. Here, we propose a “MethylSeqDesign” framework for power calculation and study design of Methyl-Seq experiments by utilizing information from pilot data. Differential methylation analysis is based on a beta-binomial model. Power calculation is achieved using mixture model fitting of p-values from pilot data and a parametric bootstrap procedure. To circumvent the issue of existing tens of millions of methylation sites, we focus on the inference of pre-specified targeted regions. The performance of the method was evaluated with simulations. Two real examples are analyzed to illustrate our method. An R package “MethylSeqDesign” to implement this method is publicly available.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

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