Clustering analysis SAGE libraries using maximal information coefficient

Abstract

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. In this study, we employ a new published measurement (maximal information coefficient, MIC) to measure the pair-wise correlation coefficients between SAGE libraries and then cluster together libraries with similar expression pattern. In addition, we present a clustering method named MicClustSAGE. 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.