Abstract David M. Reif¹, Mark A. Israel² and Jason H. Moore³

¹Graduate Student, Computational Genetics Laboratory, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756. ²Professor of Pediatrics and Genetics, Director, Norris-Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756. ³Frank Lane Research Scholar in Computational Genetics, Associate Professor of Genetics, 706 Rubin Building HB 7937; Dartmouth-Hitchcock Medical Center, One Medical Center Drive, Lebanon.

Abstract: The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a fl exible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occurring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profi les of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specifi c gene expression patterns having both statistical and biological signifi cance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the fl exibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org.

Abbreviations: EVA: Exploratory Visual Analysis; GBM: glioblastoma multiforme; GUI: graphical user interface; GO: Gene Ontology; ANOVA: analysis of variance.

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