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Open Access Highly Accessed Methodology article

Combining Affymetrix microarray results

John R Stevens1 and RW Doerge12*

Author Affiliations

1 Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, Indiana 47907-2067, USA

2 Department of Agronomy, Purdue University, Lilly Hall of Sciences, 915 W. State Street, West Lafayette, Indiana 47907-2054, USA

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BMC Bioinformatics 2005, 6:57  doi:10.1186/1471-2105-6-57

Published: 17 March 2005



As the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.


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.