title: Visual analysis of statistical results from microarray studies of human breast cancer.
abstract: Computational and statistical analysis of microarray data is a daunting challenge. Perhaps even more daunting is the biological interpretation of microarray data analysis results. We have previously developed the exploratory visual analysis (EVA) software and database for exploring data analysis results in the context of biological information on each gene available in public databases such as Entrez Gene. EVA brings a flexible combination of statistics and biological annotation to the user's desktop in a straightforward visual interface. Using a publicly available microarray dataset of gene expression response to chemotherapeutic agents in human breast cancer cell lines, we demonstrate the usefulness of the EVA system for interpreting statistical results. EVA can extend previous analyses as well as aid in making novel discoveries. Thus, we anticipate EVA will prove a useful addition to the repertoire of computational methods for microarray data analysis. The EVA software is freely available to academic users.
authors.affiliation: Computational Genetics Laboratory, Norris Cotton Cancer Center, Department of Genetics, Dartmouth Medical School, One MedicalCenter Drive, Lebanon, NH 03756, USA.
authors.forename: David M
authors.initials: DM
authors.lastname: Reif
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software
chemical_list: D000970:Antineoplastic Agents