Application of random matrix theory to identification of lung cancer gene networks

We used random matrix theory （RMT） to remove the noises in lung cancer gene expression data and used the modules approach and the hierarchical clustering approach to construct the gene networks. Comparing the results given by the two methods, we found that RMT-hierarchical clustering method gives true modules as well as the correlations between the modules. The results indicate that RMT-hierarchical clustering method is an effective new method for identifying gene networks.