Date of Award:

2015

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Mathematics and Statistics

Advisor/Chair:

John R. Stevens

Co-Advisor/Chair:

John R. Stevens

Abstract

Many researchers across a wide range of disciplines have turned to gene expression anal- ysis to aid in predicting and understanding biological outcomes and mechanisms. Because genes are known to work in a dependent manner, it’s common for researchers to first group genes in biologically meaningful sets and then test each gene set for differential expression. Comparisons are made across different treatment/condition groups. The meta-analytic method for testing differential activity of gene sets, termed multi-variate gene set testing (mvGST), will be used to provide context for two persistent and problematic issues in gene set testing. These are: 1) gathering organism specific annotation for non-model organisms and 2) handling gene annotation ambiguities. The primary purpose of this thesis is to explore different gene annotation gathering methods in the building of gene set lists and to address the problem of gene annotation ambiguity. Using an example study, three different annotation gathering methods are proposed to construct GO gene set lists. These lists are directly compared, as are the subsequent results from mvGST analysis. In a separate study, an optimization algorithm is proposed as a solution for handling gene annotation ambiguities.

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