A model-based dimension reduction approach to classification of gene expression data

Gene expression profiles of thousands of genes have proved to be particularly promising for biological classification, particularly for cancer diagnosis. However, microarray data present major challenges due to the complex, multiclass nature and the overwhelming number of variables characterizing gene expression profiles. We introduce a dimension reduction method for determining the classes among gene expression data obtained from a finite mixture of Gaussian densities. Information on the dimension reduction subspace is obtained from the variation on group means and, depending on the estimated mixture model, on the variation on group covariances. We show that the combination of Slice Inverse Regression and model based clustering is a powerful technique to find group among gene expression data.