IMR Press / FBL / Volume 13 / Issue 7 / DOI: 10.2741/2878

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.

Dimension reduction and mixed-effects model for microarray meta-analysis of cancer
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1 Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA
2 Center for Molecular Biology of Oral Diseases, College of Dentistry, University of Illinois at Chicago, Chicago, IL
3 Department of Human Genetics & Microarray Core, University of California at Los Angeles, Los Angeles, CA
4 Department of Otorhinolaryngology-Head and Neck Surgery, University of Pennsylvania Health System, Philadelphia, PA
5 UIC Cancer Center, Graduate College, University of Illinois Chicago, Chicago, IL
6 Department of Oral and Maxillofacial Surgery, the Second Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

*Author to whom correspondence should be addressed.

Academic Editor: Xiaofeng Zhou

Front. Biosci. (Landmark Ed) 2008, 13(7), 2714–2720;
Published: 1 January 2008

The rapid advances in high-throughput microarray technologies greatly facilitate the disease biomarker discovery. However, the potential of these microarray data has not yet been fully utilized. This is partly due to the limited sample sizes of each individual study. Combining microarray data from multiple studies improves the statistical power of detecting differentially expressed genes. Here we present a method for combining the microarray datasets at array probeset level. Using datasets from two commonly used array platforms, the Affymetrix Human Genome U133A and Human Genome U133 Plus 2.0 arrays, we found laboratory effects may be more influential than the platform effect. A visualization scheme for merging the array data from different array platforms was proposed to qualitatively judge the degree of agreement between datasets. A mixed-effects model was applied to identify differentially expressed genes from the merged array data.

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