Bioinformatics Advance Access first published online on January 18, 2007
This version published online on January 22, 2007
Bioinformatics, doi:10.1093/bioinformatics/btl663
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Genomic Characterization of Multiple Clinical Phenotypes of Cancer Using Multivariate Linear Regression Models
1Department of Pharmacoepidemiology, Graduate School of Public Health, Kyoto University, Kyoto, Japan, 2Translational Re-search Informatics Center, Foundation for Biomedical Research and Innovation, Kobe, Japan, 3Department of Urology, Kyoto University Graduate School of Medicine, Kyoto, Japan, 4Division of Biostatistics, School of Pharmaceutical Sciences, Kitasato University, Tokyo, Japan, 5Department of Pathology, Kyoto University Graduate School of Medicine, Kyoto, Japan, 6Pacific Edge Biotechnology, University of Otago, Dunedin, New Zealand, 7Cancer Genetics Laboratory, Department of Biochemistry, University of Otago, Dunedin, New Zealand and 8Division of Clinical Trial Design and Management, Translational Research Center, Kyoto University Hospital, Kyoto, Japan
*To whom correspondence should be addressed. Dr. Shigeyuki Matsui, E-mail: matsui{at}pbh.med.kyoto-u.ac.jp
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Abstract |
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Motivation: The development of gene expression microarray technology has allowed the identification of differentially expressed genes between different clinical phenotypic classes of cancer from a large pool of candidate genes. Although many class comparisons concerned only a single phenotype, simultaneous assessment of the relationship between gene expression and multiple phenotypes would be warranted to better understand the underlying biological structure.
Results: We develop a method to select genes related to multiple clinical phenotypes based on a set of multivariate linear regression models. For each gene, we perform model selection based on the doubly-adjusted R-square statistic and use the maximum of this statistic for gene selection. The method can substantially improve the power in gene selection, compared with a conventional method that uses a single model exclusively for gene selection. Application to a bladder cancer study to correlate pre-treatment gene expressions with pathological stage and grade is given. The methods would be useful for screening for genes related to multiple clinical phenotypes.
Availability: SAS and MATLAB codes are available from author upon request.
Associate Editor: Joaquin Dopazo
Received on October 2, 2006; revised on November 27, 2006; accepted on December 25, 2006
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