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.
M. Milad and G. R. Olbricht, "Testing Differentially Methylated Regions through Functional Principal Component Analysis," Journal of Applied Statistics, Routledge, Jan 2021.
The definitive version is available at https://doi.org/10.1080/02664763.2021.1877636
Mathematics and Statistics
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
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Keywords and Phrases
DNA methylation; epigenetics; Functional principal component; next-generation sequencing
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01 Jan 2021