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dc.contributor.authorDaniel, Jeannie T
dc.date.accessioned2017-08-03T16:22:48Z
dc.date.available2017-08-03T16:22:48Z
dc.date.issued8/3/2017en
dc.identifier.urihttp://hdl.handle.net/10675.2/621522
dc.description.abstractDNA methylation plays an important role in the regulation of gene expression, as hypermethylation is associated with gene silencing. The general purpose of this dissertation is the development of a statistical method, called DMR Detector, for detecting differentially methylated regions (DMRs) on the 450K array. DMR Detector makes three key modifications to an existing method called Bumphunter. The first is what statistic to collect from the initial fitting for further analysis. The second is to perform kernel smoothing under the assumption of correlated errors using a newly proposed correlation-adjusted kernel weight. The third is how to define regions of interest. In simulation, the method was shown to have high power comparable to Bumphunter, with consistently lower family-wise type I error rate, controlled well below the 0.1 FDR. DMR Detector was applied to real data and was able to detect one DMR that was not detected by Bumphunter.
dc.titleA modified bump hunting approach with correlation-adjusted kernel weight for detecting differentially methylated regions on the 450K arrayen
dc.typeDissertationen
dc.contributor.departmentDepartment of Biostatistics and Epidemiologyen
dc.language.rfc3066en
dc.date.updated2017-08-03T16:22:48Z
dc.description.advisorChen, Jieen
dc.description.degreeDoctor of Philosophy (Ph.D.)en
dc.description.committeeXu, Hongyan; Wang, Xiaoling; Ghosh, Santu; Linder, Daniel; Rao, Arni SR Srinivasaen
refterms.dateFOA2020-05-20T20:13:50Z
html.description.abstractDNA methylation plays an important role in the regulation of gene expression, as hypermethylation is associated with gene silencing. The general purpose of this dissertation is the development of a statistical method, called DMR Detector, for detecting differentially methylated regions (DMRs) on the 450K array. DMR Detector makes three key modifications to an existing method called Bumphunter. The first is what statistic to collect from the initial fitting for further analysis. The second is to perform kernel smoothing under the assumption of correlated errors using a newly proposed correlation-adjusted kernel weight. The third is how to define regions of interest. In simulation, the method was shown to have high power comparable to Bumphunter, with consistently lower family-wise type I error rate, controlled well below the 0.1 FDR. DMR Detector was applied to real data and was able to detect one DMR that was not detected by Bumphunter.


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