Application of Random Matrix Theory to Microarray Data for Discovering Functional Gene Modules of Hepatocellular Carcinoma
随机矩阵理论在肝癌基因功能模块识别中的应用

The function modules of hepatocellular carcinomas (HCC) gene expression network was identified by Random Matrix Theory (RMT). The standard deviation of the eigen-value spacing distribution of the expression correlation matrices to the two RMT distributions was used to identify the transition, where the random components were ultimately removed and the correlation matrix contains the clear and important modular information. By analyzing the lager 13 modules revealed by RMT, It was found that these models were closely related to the form and development of hepatocellular carcinomas. The RMT method, having the advantages of avoiding the objective effects and removing the noise caused by experiments, can be an effective way to identify gene functional modules from the complex gene expression networks. Because of the large number of genes and the complexity of cell biological processes, the systematic study of HCC using RMT from an integral perspective helps to understand the mechanisms of hepatocarcinogenesis at molecule level and to advance effective therapy methods.