title: Analysing gene expression data from DNA microarrays to identify candidate genes.
abstract: Microarray data analysis can be divided into two tasks: grouping of genes to discover broad patterns of biological behaviour, and filtering of genes to identify specific genes of interest. Whereas the gene-grouping task is largely addressed by cluster analysis, the gene-filtering task relies primarily on hypothesis testing. This review article surveys analytical methods for the gene-filtering task. Various types of data analysis are discussed for four basic types of experimental protocols: a comparison of two biological samples; a comparison of two biological conditions; each represented by a set of replicate samples; a comparison of multiple biological conditions; and analysis of covariate information.
authors.affiliation: Department of Bioinformatics, Genentech, Inc., South San Francisco, CA 94080, USA. twu@gene.com
authors.forename: T D
authors.initials: TD
authors.lastname: Wu
mesh_terms: D019295:Computational Biology; D016172:DNA Fingerprinting; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis
chemical_list: D005819:Genetic Markers