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dc.contributor.authorHuang, Hanwen
dc.contributor.authorChen, Zhongxue
dc.contributor.authorHuang, Xudong
dc.date.accessioned2013-06-12T14:46:31Z
dc.date.available2013-06-12T14:46:31Z
dc.date.issued2013-03-06
dc.identifier.citationBMC Bioinformatics. 2013 Mar 06;14(1):86
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2105-14-86
dc.identifier.urihttp://hdl.handle.net/10724/19539
dc.description.abstractAbstract Background DNA methylation profiles differ among disease types and, therefore, can be used in disease diagnosis. In addition, large-scale whole genome DNA methylation data offer tremendous potential in understanding the role of DNA methylation in normal development and function. However, due to the unique feature of the methylation data, powerful and robust statistical methods are very limited in this area. Results In this paper, we proposed and examined a new statistical method to detect differentially methylated loci for case control designs that is fully nonparametric and does not depend on any assumption for the underlying distribution of the data. Moreover, the proposed method adjusts for the age effect that has been shown to be highly correlated with DNA methylation profiles. Using simulation studies and a real data application, we have demonstrated the advantages of our method over existing commonly used methods. Conclusions Compared to existing methods, our method improved the detection power for differentially methylated loci for case control designs and controlled the type I error well. Its applications are not limited to methylation data; it can be extended to many other case–control studies.
dc.titleAge-adjusted nonparametric detection of differential DNA methylation with case--control designs
dc.typeJournal Article
dc.date.updated2013-06-07T12:46:14Z
dc.description.versionPeer Reviewed
dc.language.rfc3066en
dc.rights.holderHanwen Huang et al.; licensee BioMed Central Ltd.


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