MethylSeqDesign: a framework for Methyl-Seq genome-wide power calculation and study design issues. Biostatistics 2021 Jan 28;22(1):35-50
Date
05/21/2019Pubmed ID
31107532Pubmed Central ID
PMC7846147DOI
10.1093/biostatistics/kxz016Scopus ID
2-s2.0-85100925016 (requires institutional sign-in at Scopus site) 1 CitationAbstract
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.
Author List
Liu P, Lin CW, Park Y, Tseng GAuthor
Chien-Wei Lin PhD Associate Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Computer SimulationDNA Methylation
Genome
High-Throughput Nucleotide Sequencing
Models, Genetic
Research Design
Sequence Analysis, DNA