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The Scientific World Journal
Volume 2012 (2012), Article ID 989637, 9 pages
http://dx.doi.org/10.1100/2012/989637
Research Article

Robust Microarray Meta-Analysis Identifies Differentially Expressed Genes for Clinical Prediction

1Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA
2Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Grady Health System, Grady Memorial Hospital, Atlanta, GA 30303, USA

Received 2 November 2012; Accepted 28 November 2012

Academic Editors: N. S. T. Hirata, M. A. Kon, and K. Najarian

Copyright © 2012 John H. Phan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, and classifier (i.e., logistic regression, diagonal LDA, and linear SVM), we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.