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This paper proposes a new method for tumor classification using gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are selected using t-statistics. Secondly, the selected genes are modeled by Independent Component Analysis (ICA). Finally, Support Vector Machine (SVM) is used to classify the modeling data. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.

Previous studies on tumor classification based on feature extraction from gene expression profiles (GEP) were proven to be effective, but some of such methods lack biomedical meaning to some extent. To deal with this problem, we proposed a novel feature extraction method whose experimental results are of biomedical interpretability and helpful for gaining insight into the structure analysis of gene expression dataset. This method first applied rank sum test to roughly select a set of informative genes and then adopted factor analysis to extract latent factors for tumor classification. Experiments on three pairs of cross-platform tumor datasets indicated that the proposed method can obviously improve the performance of cross-platform classification and only several latent factors, which can represent a large number of informative genes, would obtain very high predictive accuracy on test set. The results also suggested that the classification model trained on one dataset can successfully predict another tumor dataset with the same tumor subtype obtained on different experimental platforms.

Sparse representation based classification (SRC) has been extensively and successfully applied to tumor classification due to its effectiveness in classification. Different from many existing SRC methods which emphasize on strong representational ability, our proposed method takes the discriminative ability into account, i.e. joint meta-sample extraction and classifier learning, which may be favor to better performance. In addition, a novel decision rule, in relation to both the minimal reconstruction residual and the minimum classification error, is presented and used during the classification stage. Experiments using several publicly available microarray gene expression data demonstrate that the proposed algorithm is efficient and achieves comparable accuracies with the state-of-the-art sparse coding techniques for tumor classification.