pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data
Yong Chen, Hokeun Sun, Ya Wang, Shuang Wang, Yun Li · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2020
Motivation: DNA methylation plays an important role in many biological processes and cancer progression. Recent studies have found that there are also differences in methylation variations in different groups other than differences in methylation means. Several methods have been developed that consider both mean and variance signals in order to improve statistical power of detecting differentially methylated loci. Moreover, as methylation levels of neighboring CpG sites are known to be strongly correlated, methods that incorporate correlations have also been developed. We previously developed a network-based penalized logistic regression for correlated methylation data, but only focusing on mean signals. We have also developed a generalized exponential tilt model that captures both mean and variance signals but only examining one CpG site at a time.