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Gea : subtitle a toolkit for gene expression analysis

University of British Columbia

In recent years, relating gene expression to cancer development and treatment has received a lot of attention. The precise identification of a cancer's type and stage is often crucial to the assignment of appropriate treatment. Therefore, a central goal of the analysis of gene expression data is to identify sets of genes that can be used for the classification or diagnosis of cancer. Another important purpose of gene expression studies is to improve the understanding of cellular responses to drug treatment, to identify drug responsive genes, and to discover the potential drug targets. To achieve these goals, researchers are applying various methods of analysis to gene expression data. Unfortunately, the availability of effective analysis tools lags far behind the availability of data. In this thesis, we present the Gene Expression Analyzer (GEA) for performing cluster analysis on gene expression data. In particular, the GEA is developed to address the reality that cluster analysis is typically a multi-step process. The underlying model of the GEA provides a set of algebraic operators for manipulating the data, as well as intermediate results. Moreover, the GEA provides facilities to help the user to identify candidate genes for further clinical analysis. Last but not least, the GEA is optimized to handle the high dimensionality of gene expression data, and provides user-friendly features to help the user organize and manage intermediate analysis results.

Thesis/Dissertation

application/pdf

2009-09-29

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http://hdl.handle.net/2429/13383

Computer Science

University of British Columbia

UBCV

DSpace