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dc.contributor.authorBraniff, Nathanen
dc.date2015-09-08 13:25:32.063
dc.date.accessioned2015-09-10T19:05:35Z
dc.date.available2015-09-10T19:05:35Z
dc.date.issued2015-09-10
dc.identifier.urihttp://hdl.handle.net/1974/13586
dc.descriptionThesis (Master, Computing) -- Queen's University, 2015-09-08 13:25:32.063en
dc.description.abstractProstate cancer is the most common non-dermatological cancer amongst men in the developed world. The disease is manageable if detected early; treatment is thus becoming highly individualized, placing emphasis on detection and prediction of disease prognosis. This thesis is concerned with the integrative analysis of gene expression data from prostate cancer, to reveal molecular signatures of metastases and the mechanisms of disease progression. Meta-analytic procedures are used to integrate three datasets and compare primary tumors based on metastatic outcome. Four datasets are also integrated to compare primary and metastatic tumour tissue types. This statistical integration provides a more robust and accurate characterization of gene expression signatures, and helps minimize microarray noise and study-specific effects. Multiple methods of integration are explored. Once integrated, a subset of significantly differentiated transcripts is selected to form a tentative expression signature. A support vector machine was used to construct a predictive model of metastatic outcome based on the identified expression signature. Its performance was assessed using a nested cross-validation procedure and out-of-sample testing. Data integration and network analysis have proved to be useful tools in providing context to the complex systems studied in system biology. This thesis makes use of a number of such techniques. Pathway enrichment analysis with DAVID and PathDIP were used to identify which biological pathways and related functions are most influenced by the signatures. Pathways related to extracellular matrix were found to be significantly enriched in the metastatic outcome comparison. Integrating these lists using iCTnet and Cytoscape with heterogeneous disease-gene interaction networks revealed the relationships between the expression signatures and other cancers. Integrating the data with protein interaction datasets using the I2D database and Navigator network application allowed for the more robust comparison of various integration methodologies and expression signatures beyond just simple intersection. The results of the comparisons agree well with previous findings.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsCreative Commons - Attribution - CC BYen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectbioinformaticsen
dc.subjectgene expressionen
dc.subjectmeta-analysisen
dc.subjectprostate canceren
dc.titleIntegrative Analysis of Transcriptomic Data Applied to Prostate Cancer Metastasisen
dc.typethesisen
dc.description.degreeM.Sc.en
dc.contributor.supervisorMousavi, Parvinen
dc.contributor.departmentComputingen
dc.degree.grantorQueen's University at Kingstonen


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