TY - JOUR A2 - Hirata, N. S. T. A2 - Kon, M. A. A2 - Najarian, K. AU - Phan, John H. AU - Young, Andrew N. AU - Wang, May D. PY - 2012 DA - 2012/12/18 TI - Robust Microarray Meta-Analysis Identifies Differentially Expressed Genes for Clinical Prediction SP - 989637 VL - 2012 AB - 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. SN - 2356-6140 UR - https://doi.org/10.1100/2012/989637 DO - 10.1100/2012/989637 JF - The Scientific World Journal PB - The Scientific World Journal KW - ER -