1. Genomic characterization of multiple clinical phenotypes of cancer using multivariate linear regression models
- Author
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Anthony E. Reeve, Osamu Ogawa, Masaaki Ito, Shigeyuki Matsui, Hiroyuki Nishiyama, Parry Guilford, Hajime Uno, Hirokazu Kotani, Jun Watanabe, and Masanori Fukushima
- Subjects
Statistics and Probability ,Candidate gene ,Multivariate analysis ,Biology ,Biochemistry ,Bayesian multivariate linear regression ,Biomarkers, Tumor ,Humans ,Computer Simulation ,Diagnosis, Computer-Assisted ,Molecular Biology ,Gene ,Oligonucleotide Array Sequence Analysis ,Genetics ,Models, Genetic ,Gene Expression Profiling ,Model selection ,Linear model ,Chromosome Mapping ,Phenotype ,Neoplasm Proteins ,Computer Science Applications ,Gene expression profiling ,Computational Mathematics ,Urinary Bladder Neoplasms ,Computational Theory and Mathematics ,Multivariate Analysis ,Linear Models ,Regression Analysis - Abstract
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.Contact: matsui@pbh.med.kyoto-u.ac.jp
- Published
- 2007
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