MethylSeqDesign: a framework for Methyl-Seq genome-wide power calculation and study design issues. (20th May 2019)

• Record Type: Journal Article
• Authors: Liu, Peng; Lin, Chien-Wei; Park, Yongseok; Tseng, George
• Is Part Of: Biostatistics. Volume 22:Number 1(2021)
• Page Start: 35
• Page End: 50
• Publication Date: 2019-05-20
• Subjects: Bisulfite sequencing -- Methyl-Seq -- Power calculation -- Study design; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxz016
• Languages: English
• ISSNs: 1465-4644

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.