M3D: a kernel-based test for shape changes in methylation profiles

TL;DR

The paper introduces M3D, a non-parametric kernel-based statistical test designed to detect complex shape changes in DNA methylation profiles across regions, accounting for coverage differences and outperforming existing methods.

Contribution

M3D is a novel kernel-based method that improves detection of shape changes in methylation profiles, addressing spatial correlation and coverage confounders.

Findings

Increased power over existing methods

Robust to coverage and replication variations

Effective in real and simulated datasets

Abstract

DNA methylation is an intensely studied epigenetic mark implicated in many biological processes of direct clinical relevance. While sequencing based technologies are increasingly allowing high resolution measurements of DNA methylation, statistical modelling of such data is still challenging. In particular, statistical identification of differentially methylated regions across different conditions poses unresolved challenges in accounting for spatial correlations within the statistical testing procedure. We propose a non-parametric, kernel-based method, M3D, to detect higher-order changes in methylation profiles, such as shape, across pre-defined regions. The test statistic explicitly accounts for differences in coverage levels between samples, thus handling in a principled way a major confounder in the analysis of methylation data. Empirical tests on real and simulated data sets show an increased power compared to established methods, as well as considerable robustness with respect to coverage and replication levels. Code is implemented in R, and is freely available via Bioconductor package M3D.