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dc.contributor.supervisor Hu, Pingzhao (Biochemistry and Medical Genetics) en_US
dc.contributor.author Zhao, Kaiqiong
dc.date.accessioned 2016-09-16T15:59:17Z
dc.date.available 2016-09-16T15:59:17Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/1993/31796
dc.description.abstract Gene set enrichment analysis aims to discover sets of genes, such as biological pathways or protein complexes, which may show moderate but coordinated differentiation across experimental conditions. The existing gene set enrichment approaches utilize single gene statistic as a measure of differentiation for individual genes. These approaches do not utilize any inter-gene correlations, but it has been known that genes in a pathway often interact with each other. Motivated by the need for taking gene dependence into account, we propose a novel gene set enrichment algorithm, where the gene-gene correlation is addressed via a gene-pair representation strategy. Relying on an appropriately defined gene pair statistic, the gene set statistic is formulated using a competitive null hypothesis. Extensive simulation studies show that our proposed approach can correctly control the type I error (false positive rate), and retain good statistical power for detecting true differential expression. The new method is also applied to analyze several gene expression datasets. en_US
dc.subject Gene set enrichment analysis en_US
dc.subject Inter-gene correlation en_US
dc.subject Gene set summary statistic en_US
dc.subject Gene-pair statistic en_US
dc.title Gene-pair based statistical methods for testing gene set enrichment in microarray gene expression studies en_US
dc.degree.discipline Biochemistry and Medical Genetics en_US
dc.contributor.examiningcommittee Liu, Xiao-Qing (Biochemistry and Medical Genetics) Acar, Elif (Statistics) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2016 en_US


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