Exploratory Visual Analysis of statistical results from microarray experiments comparing high and low grade glioma

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 flexible 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 profiles 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-specific gene expression patterns having both statistical and biological significance. 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 flexibility 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.

Keywords: annotation databases; biological interpretation; gene expression microarray; glioma; statistics; visualization.

Figure 1.

Genes whose expression levels discriminate between GBMs and pilocytic astrocytomas were identified using a combination of analysis of variance (ANOVA) and fold-change differences between tumor classes. These results were given to EVA as three separate statistics for each gene on the array: 1) the F test p-value, 2) the gene coded according to whether it had a significant fold-change and p-value < 0.01, and 3) the gene coded according to whether it had a significant negative fold-change and p-value < 0.01 or significant positive fold-change and p-value < 0.01.

Figure 2.

Each square represents the p-value for a particular gene, organized according to Gene Ontology. The “cell adhesion” and “cell proliferation” groups have a relatively high number of highlighted gene squares, meaning that they contain a number of genes that have a fold-change difference greater than 1.5 and p-value < 0.01. The results of permutation tests that statistically verify this visual impression are listed in the log.