Gene expression data classification using consensus independent component analysis

Abstract

We propose a new method for tumor classification from gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are modeled by independent component analysis (ICA). Secondly, the most discriminant eigenassays extracted by ICA are selected by the sequential floating forward selection technique. Finally, support vector machine is used to classify the modeling data. To show the validity of the proposed method, we applied it to classify three DNA microarray datasets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

Fig. 1

The gene expression data synthesis model. To find a set of independent basis snapshots (eigenassay), the snapshots in X are considered to be a linear combination of statistically independent basis snapshots (the rows in S), where W is the unmixing matrix and A is an unknown mixing matrix. The independent eigenassay is estimated as the output U of the learned ICA.