title: Comparative microarray analysis.
abstract: Microarrays enable high-throughput parallel gene expression analysis, and their use has grown exponentially during the past decade. We are now in a position where individual experiments could benefit from using the swelling public data repositories to allow microarrays to progress from being a hypothesis-generating tool to a powerful resource that can be used to test hypothesis about biology. Comparative microarray analysis could better distinguish phenotypes from associated phenotypes; identify valid differentially expressed genes by combining many studies; test new hypothesis; and discover fundamental patterns of gene regulation. This review aims to describe the additional methodology needed for such comparative microarray analysis, and we identify and discuss a number of problems such as loss of published data, lack of annotations, and variable array quality, which need to be solved before comparative microarray analysis can be used in a more systematic and powerful manner.
authors.affiliation: Department of Medicine, University of Minnesota, Minneapolis, Minnesota 55455, USA. larss004@tc.umn.edu
authors.forename: Ola
authors.initials: O
authors.lastname: Larsson
authors.forename: Kristian
authors.initials: K
authors.lastname: Wennmalm
authors.forename: Rickard
authors.initials: R
authors.lastname: Sandberg
mesh_terms: D000818:Animals; D006801:Humans; D015201:Meta-Analysis as Topic; D046228:Microarray Analysis