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

• Type of Material: Article
• Authors: John Phan, Emory University; Andrew Young, Emory University; Dongmei Wang, Emory University
• Language: English
• Date: 2012-12-18
• Publisher: Hindawi Publishing Corporation
• Publication Version: Final Published Version
• Copyright Statement: © 2012 John H. Phan et al.
• License: Creative Commons Attribution 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1100/2012/989637
• Title of Journal or Parent Work: Scientific World Journal
• ISSN: 2356-6140
• Volume: 2012
• Start Page: 989637
• End Page: 989637
• Grant/Funding Information: The funding sources listed here have supported this multiyear investigation of microarray meta-analysis for clinical prediction, including covering the stipends and salaries of multiple coauthors, computing hardware and software licenses, travel expenses to technical meetings to present this work, and publication expenses.; This work was supported in part by Grants from National Institutes of Health (Bioengineering Research Partnership R01CA108468, Center for Cancer Nanotechnology Excellence U54CA119338); Georgia Cancer Coalition (Distinguished Cancer Scholar Award to M. D. Wang); Hewlett Packard; and Microsoft Research.
• Keywords: CELLS; Science & Technology; BREAST-CANCER; METASTASIS; TUMORS; CARCINOMA; PATTERNS; SIGNATURE; Multidisciplinary Sciences; MULTIDISCIPLINARY SCIENCES; INVASION; SURVIVAL; Science & Technology - Other Topics; DIAGNOSIS
• Research Categories: Engineering, Biomedical; Health Sciences, Pathology; Biology, Genetics

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.