Incorporating increased variability in testing for cancer DNA methylation

TL;DR

This paper introduces joint constrained statistical tests that incorporate increased variability in DNA methylation data to improve cancer biomarker detection, outperforming standard methods in simulations and real TCGA data.

Contribution

It develops likelihood ratio-based joint tests for differential mean and variance in methylation, with efficient algorithms and implementation in an R package.

Findings

Enhanced detection of cancer methylation markers in simulations.

Significantly increased candidate CpG markers in TCGA data.

Improved power over standard tests in diverse models.

Abstract

Cancer development is associated with aberrant DNA methylation, including increased stochastic variability. Statistical tests for discovering cancer methylation biomarkers have focused on changes in mean methylation. To improve the power of detection, we propose to incorporate increased variability in testing for cancer differential methylation by two joint constrained tests: one for differential mean and increased variance, the other for increased mean and increased variance. To improve small sample properties, likelihood ratio statistics are developed, accounting for the variability in estimating the sample medians in the Levene test. Efficient algorithms were developed and implemented in DMVC function of R package DMtest. The proposed joint constrained tests were compared to standard tests and partial area under the curve (pAUC) for the receiver operating characteristic curve (ROC) in simulated datasets under diverse models. Application to the high-throughput methylome data in The Cancer Genome Atlas (TCGA) shows substantially increased yield of candidate CpG markers.