Multiple testing using the posterior probability of half-space : application to gene expression data (Book, 2005) [WorldCat.org]
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Multiple testing using the posterior probability of half-space : application to gene expression data
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Multiple testing using the posterior probability of half-space : application to gene expression data

Author: Aurélie Labbe; University of Waterloo. Department of Statistics and Actuarial Science.
Publisher: Waterloo, Ont. : University of Waterloo, 2005.
Dissertation: Ph. D. University of Waterloo 2005
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : EnglishView all editions and formats
Summary:
We consider the problem of testing the equality of two sample means, when the number of tests performed is large. Applying this problem to the context of gene expression data, our goal is to detect a set of genes differentially expressed under two treatments or two biological conditions. A null hypothesis of no difference in the gene expression under the two conditions is constructed. Since such a hypothesis is  Read more...
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Genre/Form: Academic Dissertation
Academic theses
Thèses et écrits académiques
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Aurélie Labbe; University of Waterloo. Department of Statistics and Actuarial Science.
OCLC Number: 71500168
Notes: "A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Doctor of Philosophy in Statistics."
Description: 1 online resource
Details: Mode of access: World Wide Web.; System requirements: Internet connectivity and World Wide Web browser. Adobe Acrobat reader required to view and print files.
Responsibility: by Aurélie Labbe.

Abstract:

We consider the problem of testing the equality of two sample means, when the number of tests performed is large. Applying this problem to the context of gene expression data, our goal is to detect a set of genes differentially expressed under two treatments or two biological conditions. A null hypothesis of no difference in the gene expression under the two conditions is constructed. Since such a hypothesis is tested for each gene, it follows that thousands of tests are performed simultaneously, and multiple testing issues then arise. The aim of our research is to make a connection between Bayesian analysis and frequentist theory in the context of multiple comparisons by deriving some properties shared by both p-values and posterior probabilities. The ultimate goal of this work is to use the posterior probability of the one-sided alternative hypothesis (or equivalently, posterior probability of the half-space) in the same spirit as a p-value. We show for instance that such a Bayesian probability can be used as an input in some standard multiple testing procedures controlling for the False Discovery rate.

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