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Open Access Methodology article

MAID : An effect size based model for microarray data integration across laboratories and platforms

Ivan Borozan1*, Limin Chen12, Bryan Paeper7, Jenny E Heathcote6, Aled M Edwards123, Michael Katze7, Zhaolei Zhang12 and Ian D McGilvray45*

Author Affiliations

1 Banting and Best Department of Medical Research, University of Toronto, 112 College St, Toronto, ON M5G1L6, Canada

2 Department of Medical Genetics and Microbiology, University of Toronto, 1 King's College Circle, Toronto, ON M5S1A8, Canada

3 Department of Medical Biophysics, University of Toronto, 610 University Avenue, Toronto, ON M5G2M9, Canada

4 Toronto General Research Institute, 610 University Avenue, Toronto, ON M5G2M9, Canada

5 Department of Surgery, Faculty of Medicine, University of Toronto, 1 King's College Circle, Toronto, ON M5S1A8, Canada

6 Toronto Western Hospital, 399 Bathurst St, Toronto, ON M5T2S8, Canada

7 Department of Microbiology, Box 357242, University of Washington, Seattle, WA 98195-7242, USA

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BMC Bioinformatics 2008, 9:305  doi:10.1186/1471-2105-9-305

Published: 10 July 2008

Abstract

Background

Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions.

Results

Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated.

Conclusion

High-throughput genomics data provide a rich and complex source of information that could play a key role in deciphering intricate molecular networks behind disease. Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.