Estrogen receptor status prediction by gene component regression: a comparative study

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.