Cluster serial analysis of gene expression data with maximal information coefficient model.

Serial analysis of gene expression (SAGE) is an efficient technique to produce a snapshot of the messenger RNA population in a sample. Clustering method has been widely used for SAGE data mining. Clustering SAGE data into different pattern groups can help to find potentially unknown functional gene groups in SAGE dataset. By incorporating a new published measurement (maximal information coefficient, MIC) into hierarchical clustering techniques, we present a clustering method named MicClustSAGE. The MIC can measure the pair-wise correlation coefficients between SAGE libraries. The presented method significant improvements the ability of clustering method in detecting specially tissue pattern of SAGE. In addition, we compared the results obtained by our method and hierarchical clustering with Pearson correlation. The experimental results exhibit the performance of the proposed method on several real-life SAGE datasets.