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 occuring 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.

Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission

“…Our PBA was conducted using exploratory visual analysis (EVA), developed and maintained by Moore and colleagues at Dartmouth Medical School, for PBA and visualization of multiple types of data, including WGA results (Reif et al 2007). BrieXy this technique enables a biologically-informed statistical prioritization of analytic results, employing any type or combination of genomic or proteomic data, and producing a list of gene sets statisticallyranked according to their association with disease risk.…”

“…EVA was developed to address the limitations of other analytic approaches to genomic results Reif et al 2007;Reif and Moore 2006). The software can take as input any kind of statistical result(s) for any number of experiments, and the user can choose any statistic or deWne a custom statistic.…”

Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing

“…Wang et al (2007) published a study applying a modified GSEA algorithm to analyze individual-level genotype data from a GWAS while many additional investigations have elaborated on the relative strengths of gene set-based approaches in genetic discovery (Al-Shahrour et al 2007; Chen et al 2009; Medina et al 2009). As a result, GSA methods and applications designed to apply similar algorithms to marker-level summary results (e.g., marker-level p values) of GWAS have emerged [e.g., gene set-based analysis of polymorphisms (GeSBAP) (Medina et al 2009), Exploratory visual analysis (EVA) (Reif et al 2005, 2007), improved gene set enrichment analysis (iGSEA) (Zhang et al 2010), GSA-SNP (Nam et al 2010), exploratory gene association network (EGAN) (Paquette and Tokuyasu 2010)].…”

Complex systems analysis of bladder cancer susceptibility reveals a role for decarboxylase activity in two genome-wide association studies

“…We then performed a gene set enrichment analysis to determine if there were more SNPs with p-values at or below the 0.05 significance level in gene regions than would be expected given their size. This was accomplished using a right-tailed Fisher’s exact test implemented in Exploratory Visual Analysis (EVA) [12, 13]. These P -values for SNP overabundance were then assigned to each gene.…”