Gene ontology assisted exploratory microarray clustering and its application to cancer

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 meaningfull clusters. We have validated our method using a multi-cancer microarray dataset. In addition, we show the potential of such methods for the exploration of cancer etiology.