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dc.contributor.advisorPowers, Scotten_US
dc.contributor.authorSu, Yien_US
dc.contributor.otherDepartment of Applied Mathematics and Statisticsen_US
dc.date.accessioned2013-05-22T17:35:40Z
dc.date.available2013-05-22T17:35:40Z
dc.date.issued1-Dec-12en_US
dc.date.submitted12-Decen_US
dc.identifierSu_grad.sunysb_0771E_11231en_US
dc.identifier.urihttp://hdl.handle.net/1951/59879
dc.description68 pg.en_US
dc.description.abstractDNA copy number change and epigenetic alteration often induce abnormal RNA expression level and have been linked to the development and progression of cancer. While various methods have been proposed for studying microarray DNA copy number and RNA expression data respectively, little statistical work has been done in modeling the relationship between the two. We propose for the joint analysis of the two types of data a new stochastic change-point model with latent variables, and an associated estimation procedure. Our method integrates hidden Markov model with Bayesian statistics to yield joint posterior distribution of DNA and RNA signal intensities throughout the whole genome. Explicit formulas of the posterior means are derived, which can be used to give direct estimates of the signal intensities without performing segmentation. A subsequent segmentation procedure is further provided to identify change-points and yield piecewise constant estimates of the signal intensities on each segment. Other quantities can also be derived from the posterior distribution for assessing the confidence of coincident and non-coincident change-points in the DNA and RNA sequences. Based on these estimates, chromosomal regions with genetic and potential epigenetic aberrations can be identified. For computational simplicity we propose an approximation method to keep computation time linear in sequence length, hence the method can be readily applied to the new generation of higher-throughput arrays. The proposed method is illustrated through simulation studies and application to a real data set.en_US
dc.description.sponsorshipStony Brook University Libraries. SBU Graduate School in Department of Applied Mathematics and Statistics. Charles Taber (Dean of Graduate School).en_US
dc.formatElectronic Resourceen_US
dc.language.isoen_USen_US
dc.publisherThe Graduate School, Stony Brook University: Stony Brook, NY.en_US
dc.subject.lcshStatisticsen_US
dc.subject.otherBayes Theory, Markov Chain, Segmentationen_US
dc.titleA Stochastic Segmentation Model for Joint DNA-RNA Microarray Data Analysisen_US
dc.typeDissertationen_US
dc.description.advisorAdvisor(s): Powers, Scott . Committee Member(s): Green, David ; Powers, Scott ; Xing, Haipeng ; Zhu, Wei ; Shen, Ronglaien_US
dc.mimetypeApplication/PDFen_US


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