title: Exploratory visual analysis of pharmacogenomic results.
abstract: Comprehensive analysis of expansive pharmacogenomic datasets is a daunting challenge. A thorough exploration of experimental results requires both statistical and annotative information. Therefore, appropriate analysis tools must bring a readily-accessible, flexible combination of statistics and biological annotation to the user's desktop. We present the Exploratory Visual Analysis (EVA) software and database as such a tool and demonstrate its utility in replicating the findings of an earlier pharmacogenomic study as well as elucidating novel biologically plausible hypotheses. EVA brings all of the often disparate pieces of analysis together in an infinitely flexible visual display that is amenable to any type of statistical result and biological question. Here, we describe the motivations for developing EVA, detail the database and custom graphical user interface (GUI), provide an example of its application to a publicly available pharmacogenomic dataset, and discuss the broad utility of the EVA tool for the pharmacogenomics community.
authors.affiliation: Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH 03756, USA. reif@chgr.mc.vanderbilt.edu
authors.forename: David M
authors.initials: DM
authors.lastname: Reif
authors.forename: Scott M
authors.initials: SM
authors.lastname: Dudek
authors.forename: Christian M
authors.initials: CM
authors.lastname: Shaffer
authors.forename: Janey
authors.initials: J
authors.lastname: Wang
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D019295:Computational Biology / Q000379:methods; D016208:Databases, Factual; D030561:Databases, Nucleic Acid; D008838:Microcomputers; D008957:Models, Genetic; D015233:Models, Statistical; D010597:Pharmacogenetics / Q000379:methods*