Empirical Bayes Methods for Proteomics

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dc.contributor.advisorSeillier-Moiseiwitsch, Francoise
dc.contributor.advisorRukhin, Andrew
dc.contributor.authorLi, Feng
dc.contributor.departmentMathematics and Statistics
dc.contributor.programStatistics
dc.date.accessioned2015-10-14T03:11:33Z
dc.date.available2015-10-14T03:11:33Z
dc.date.issued2008-02-05
dc.description.abstractProteomics is the science that deals with high-throughput analysis of proteins. The study of proteins relies on efficient protein separation technique. 2D PAGE is a powerful technique for separating complex mixtures of proteins, where thousands of proteins are separated and measured simultaneously. The analysis of 2D PAGE images needs efficient methods able to cope with large-scale dataset. Empirical Bayes methods have been shown to be very efficient at combining information across dimensions of high-dimensional data. In the first part of this dissertation, the construction of empirical Bayes confidence intervals under different model assumptions is studied. Numerical simulations are conducted to demonstrate the satisfactory performance of the proposed methods. In the second part, a new comprehensive procedure for statistical analysis of 2D PAGE images is proposed, including protein quantification, normalization and statistical analysis. It reduces the dimension of the data. It also bypasses the current bottleneck in the analysis of 2D PAGE images in that it does not require spot matching. A strategy for multiple hypothesis testing based on multivariate analysis combined with empirical Bayes methods is formulated and applied to the differential analysis of 2D PAGE images. The new methodologies are implemented in a custom software package.
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dc.genredissertations
dc.identifierdoi:10.13016/M2HM3N
dc.identifier.other1109
dc.identifier.urihttp://hdl.handle.net/11603/1008
dc.languageen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Theses and Dissertations Collection
dc.relation.ispartofUMBC Graduate School Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Mathematics and Statistics Department Collection
dc.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu.
dc.sourceOriginal File Name: umi-umbc-1109.pdf
dc.subjectEmpirical Bayes
dc.subject2D PAGE
dc.subjectProteomics
dc.subjectConfidence interval
dc.subjectImage processing
dc.subjectStatistics (0463)
dc.subjectBiology, Bioinformatics (0715)
dc.titleEmpirical Bayes Methods for Proteomics
dc.typeText
dcterms.accessRightsAccess limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan through a local library, pending author/copyright holder's permission.

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