Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology

Gene expression profiling provides insight into the functions of genes at a molecular level. Clustering of gene expression profiles can facilitate the identification of the underlying driving biological program causing genes' co-expression. Standard clustering methods, grouping genes based on similar expression values, fail to capture weak expression correlations potentially causing genes in the same biological process to be grouped separately. We have developed a novel clustering algorithm, which incorporates functional gene information from the Gene Ontology into the clustering process, resulting in more biologically meaningful clusters. We have validated our method using two multi-cancer microarray datasets. In addition, we show the potential of such methods for the exploration of cancer etiology.