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TitlePotts model clustering for discovering patterns of epigenetic marks
NameLi, Junyi (author), Sengupta, Anirvan (chair), Bartynski, Robert (internal member), Sellwood, Jerry (internal member), Bhanot, Gyan (internal member), Pirrotta, Vincenzo (outside member), Rutgers University, Graduate School - New Brunswick,
Degree Date2011-01
Date Created2011
SubjectPhysics and Astronomy,
Epigenesis--Research,
Histones,
Cluster analysis
DescriptionStudy of epigenetics leads to understanding of the regulation of gene expression not caused by the changes in the underlying DNA sequences. This area of biological research has drawn much interest as large amounts of epigenetic data from numerous experiments were generated in recent past. In this thesis, we use the Potts model clustering method, which is based on statistical mechanics, to discover patterns in histone modification data. After a general overview of the epigenome and an introduction to common methods of clustering, we discuss why we need special cluster analysis methods for the data at hand. Then, we introduce our tool of choice, namely, the superparamagnetic Potts clustering method. We discuss the Potts model and the Swendsen-Wang method of Monte Carlo simulation, which avoids the usual slowing down experienced near phase transition. We apply Potts model clustering to histone modification data in highly conserved regions to discover the patterns of epigenetic marks and compare them with background reading. We also contrast the results from our method with that from usual Kmeans clustering approach. Finally, we discuss the biological significance of our computational results.
NotePh.D.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Junyi Li
Genretheses
Persistent URLhttp://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057612
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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