title: methylDMV: SIMULTANEOUS DETECTION OF DIFFERENTIAL DNA METHYLATION AND VARIABILITY WITH CONFOUNDER ADJUSTMENT.
abstract: DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV. The proposed framework also allows for prioritization and selection of candidate features to be included in the prediction algorithm. We illustrate the utility of methylDMV in several TCGA datasets. An R package methylDMV implementing our proposed method is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#methylDMV.
authors.affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
authors.forename: Pei Fen
authors.initials: PF
authors.lastname: Kuan
authors.affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
authors.forename: Junyan
authors.initials: J
authors.lastname: Song
authors.affiliation: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
authors.forename: Shuyao
authors.initials: S
authors.lastname: He
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D018899:CpG Islands; D019175:DNA Methylation; D004273:DNA, Neoplasm / Q000235:genetics; D030561:Databases, Nucleic Acid; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics; D012984:Software
chemical_list: D004273:DNA, Neoplasm