Detection of epigenetic changes using ANOVA with spatially varying coefficients

Guanghua Xiao, Xinlei Wang, Quincey LaPlant, Nestler Eric J. and Xie Yang ()
Additional contact information
Guanghua Xiao: Division of Biostatistics, Department of Clinical Sciences, The University of Texas Southwestern Medical Center at Dallas, TX 75390, USA
Xinlei Wang: Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA
Quincey LaPlant: Fishberg Department of Neuroscience, Mount Sinai School of Medicine, New York, NY 10029, USA
Nestler Eric J.: Fishberg Department of Neuroscience, Mount Sinai School of Medicine, New York, NY 10029, USA
Xie Yang: Division of Biostatistics, Department of Clinical Sciences, The University of Texas Southwestern Medical Center at Dallas, TX 75390, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 2, 189-205

Abstract: Identification of genome-wide epigenetic changes, the stable changes in gene function without a change in DNA sequence, under various conditions plays an important role in biomedical research. High-throughput epigenetic experiments are useful tools to measure genome-wide epigenetic changes, but the measured intensity levels from these high-resolution genome-wide epigenetic profiling data are often spatially correlated with high noise levels. In addition, it is challenging to detect genome-wide epigenetic changes across multiple conditions, so efficient statistical methodology development is needed for this purpose. In this study, we consider ANOVA models with spatially varying coefficients, combined with a hierarchical Bayesian approach, to explicitly model spatial correlation caused by location-dependent biological effects (i.e., epigenetic changes) and borrow strength among neighboring probes to compare epigenetic changes across multiple conditions. Through simulation studies and applications in drug addiction and depression datasets, we find that our approach compares favorably with competing methods; it is more efficient in estimation and more effective in detecting epigenetic changes. In addition, it can provide biologically meaningful results.

Keywords: AR1; autoregressive; Bayesian hierarchical model; epigenetic changes (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2012-0057 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:12:y:2013:i:2:p:189-205:n:1003

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2012-0057

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().