Combining Affymetrix microarray results

We propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis.The quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest.Microarray technology allows simultaneous assessment of transcript abundance for thousands of genes. This exciting research tool permits the identification of genes which are significantly differentially expressed between conditions. With the use of microarrays becoming more commonplace, it is not unusual for several different laboratories to investigate the genetic implications of the same condition(s). Each lab may produce its own list of candidate genes which they believe to be related to the condition of interest. As a result of sound statistical approaches, each lab will also have for each candidate gene some quantitative measure that serves as the basis for the claim of statistical significance.Of interest in this paper are the methods by which these quantitative measures may be combined across labs to arrive at a more comprehensive understanding of