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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2010

Open Access Proceedings

Integrated analysis of the heterogeneous microarray data

Sung Gon Yi1 and Taesung Park2*

Author Affiliations

1 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA

2 Department of Statistics, Seoul National University, Seoul, South Korea

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BMC Bioinformatics 2011, 12(Suppl 5):S3  doi:10.1186/1471-2105-12-S5-S3

Published: 27 July 2011

Abstract

Background

As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets.

Results

The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches.

Conclusions

The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.