pETM: a penalized Exponential Tilt Model for analysis of correlated high-dimensional DNA methylation data.
BACKGROUND
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.


RESULTS
In this article, we proposed a penalized Exponential Tilt Model (pETM) using network-based regularization that captures both mean and variance signals in DNA methylation data and takes into account the correlations among nearby CpG sites. By combining the strength of the two models we previously developed, we demonstrated the superior power and better performance of the pETM method through simulations and the applications to the 450K DNA methylation array data of the four breast invasive carcinoma cancer subtypes from The Cancer Genome Atlas (TCGA) project. The developed pETM method identifies many cancer-related methylation loci that were missed by our previously developed method that considers correlations among nearby methylation loci but not variance signals.


METHODS
The R package 'pETM' is publicly available through CRAN: http://cran.r-project.org .


BACKGROUND
sw2206@columbia.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Statistics, Pusan National University, Busan, Korea.
Hokeun
H
Sun
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Ya
Y
Wang
Division of Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Yong
Y
Chen
Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.|Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.|Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
Yun
Y
Li
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA.
Shuang
S
Wang
D001943:Breast Neoplasms / Q000235:genetics; D018899:CpG Islands; D019175:DNA Methylation; D005260:Female; D023281:Genomics / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software