Date of Award

Fall 2002

Document Type

Thesis

Degree Name

Master of Science in Computational Biology - (M.S.)

Department

Federated Department of Biological Sciences

First Advisor

Michael Recce

Second Advisor

Jeffrey M. Liebman

Third Advisor

Peter P. Tolias

Abstract

The purpose of this study was to determine the differences in the gene expression analysis methods of two data mining tools, ExpressionisticTM 3.1 and GeneSpringTM 4.2 with focus on basic statistical analysis and clustering algorithms. The data for this analysis was derived from the hybridization of Rattus norvegicus RNA to the Affymetrix RG34A GeneChip. This analysis was derived from experiments designed to identify changes in gene expression patterns that were induced in vivo by an experimental treatment.

The tools were found to be comparable with respect to the list of statistically significant genes that were up-regulated by more than two fold. Approximately 78% of this gene list was present in both tools. ExpressionistTm 3.1 was capable of representing the different linkage methods of hierarchical clustering as average, complete and single, whereas in GeneSpringTM 4.2, the user could manipulate the separation ratio and minimum distance of the hierarchical tree.

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