Gene Vector Analysis (Geneva): A unified method to detect differentially-regulated gene sets and similar microarray experiments

Background: Microarray experiments measure changes in the expression of thousands of genes. The resulting lists of genes with changes in expression are then searched for biologically related sets using several divergent methods such as the Fisher Exact Test (as used in multiple GO enrichment tools), Parametric Analysis of Gene Expression (PAGE), Gene Set Enrichment Analysis (GSEA), and the connectivity map. Results: We describe an analytical method (Geneva: Gene Vector Analysis) to relate genes to biological properties and to other similar experiments in a uniform way. This new method works on both gene sets and on gene lists/vectors as input queries, and can effectively query databases consisting of sets of biologically related sets, or of results from other microarray experiments. We also present an improvement to the null model estimate by using the empirical background distribution drawn from previous experiments. We validated Geneva by rediscovering a number of previous findings,...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Stephen W. Tanner, Pankaj Agarwal
• Keywords: BMCBI 2008, Gene, Gene Sets, Related Sets