Estrogen receptor status prediction by gene component regression: a comparative study Online publication date: Tue, 21-Oct-2014
by Chi-Cheng Huang; Shih-Hsin Tu; Heng-Hui Lien; Jaan-Yeh Jeng; Jung-Sen Liu; Ching-Shui Huang; Liang-Chuan Lai; Eric Y. Chuang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 9, No. 2, 2014
Abstract: The aim of the study is to evaluate gene component analysis for microarray studies. Three dimensional reduction strategies, Principle Component Regression (PCR), Partial Least Square (PLS) and Reduced Rank Regression (RRR) were applied to publicly available breast cancer microarray dataset and the derived gene components were used for tumour classification by Logistic Regression (LR) and Linear Discriminative Analysis (LDA). The impact of gene selection/filtration was evaluated as well. We demonstrated that gene component classifiers could reduce the high-dimensionality of gene expression data and the collinearity problem inherited in most modern microarray experiments. In our study gene component analysis could discriminate Estrogen Receptor (ER) positive breast cancers from negative cancers and the proposed classifiers were successfully reproduced and projected into independent microarray dataset with high predictive accuracy.
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