Affiliations: [a] Department of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan | [b] Department of Boiostatics, Moffitt Cancer Center and Research Institute, Tampa, FL, USA | University of Texas, Health Science Center at Houston, TX, USA
Correspondence:
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Corresponding author: Tzu-Hsin Liu, Ph.D., Department of Applied Mathematics, National Chung Hsing University, Taichung 402, Taiwan. Tel.: +88 6422 4650 28; E-mail: [email protected].
Abstract: One important application of the statistical analysis of microarray gene expression data is to predict the clinical outcomes of diseased patients. The accurate selection of significant genes is crucial to establishing an effective predictive model. Statistical p-value and Cox score are used to rank the lung cancer patients' genes, and principal component analysis, supervised principal components and the partial least squares methods are used to study the effect of the number of top ranked genes. Simulations are performed to evaluate the performance of the proposed methods. A real-life dataset is analyzed using the proposed methods, which are compared to each other. The predictive performance of each method is evaluated using three evaluation criteria. The results show that our predictive methods that involve gene selection have better predictive performance then other currently used methods [5].
