Testing Differentially Methylated Regions through Functional Principal Component Analysis


DNA methylation is an epigenetic modification that plays an important role in many biological processes and diseases. Several statistical methods have been proposed to test for DNA methylation differences between conditions at individual cytosine sites, followed by a post hoc aggregation procedure to explore regional differences. While there are benefits to analyzing CpGs individually, there are both biological and statistical reasons to test entire genomic regions for differential methylation. Variability in methylation levels measured by Next-Generation Sequencing (NGS) is often observed across CpG sites in a genomic region. Evaluating meaningful changes in regional level methylation profiles between conditions over noisy site-level measurements is often difficult to implement with parametric models. To overcome these limitations, this study develops a nonparametric approach to detect predefined differentially methylated regions (DMR) based on functional principal component analysis (FPCA). The performance of this approach is compared with two alternative methods (GIFT and M3D), using real and simulated data.


Mathematics and Statistics

Research Center/Lab(s)

Center for High Performance Computing Research

Second Research Center/Lab

Intelligent Systems Center

Publication Status

Latest articles: Article accepted for publication in this journal but not yet published in a volume/issue.

Keywords and Phrases

DNA methylation; epigenetics; Functional principal component; next-generation sequencing

International Standard Serial Number (ISSN)

0266-4763; 1360-0532

Document Type

Article - Journal

Document Version


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© 2021 The Authors, All rights reserved.

Publication Date

01 Jan 2021