methylDMV: SIMULTANEOUS DETECTION OF DIFFERENTIAL DNA METHYLATION AND VARIABILITY WITH CONFOUNDER ADJUSTMENT.

Pac Symp Biocomput

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.

Published: March 2017

AI Article Synopsis

  • DNA methylation is being recognized as an important marker for diagnosing diseases, with both differential mean (DM) and differential variability (DV) playing roles in disease progression.
  • The presence of confounding factors in large-scale studies can skew methylation data and complicate the identification of effective markers.
  • The paper presents a framework called methylDMV that adjusts for these confounding factors, allowing for the categorization of CpGs with DM, DV, or both, and supports the selection of key features for predictive modeling; this framework is demonstrated using various datasets from the TCGA.

Article 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.

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Source
http://dx.doi.org/10.1142/9789813207813_0043DOI Listing

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