Mining functional biclusters of DNA microarray gene expression data
A subset of genes sharing compatible expression patterns under a subset of conditions can be found from DNA microarray data using biclustering algorithms. In this paper, we present a novel geometrical biclustering algorithm in combination with gene ontology annotations to identify the gene functional biclusters. Unlike many existing biclustering algorithms, we first consider the biclustering patterns through geometrical interpretation. Such a perspective makes it possible to unify the formulation of different types of biclusters as hyperplanes in spatial space and facilitates the use of a generic plane finding algorithm for bicluster detection. In our bottom-up biclustering algorithm, the well-known Hough transform is first employed in pair-column spaces to reduce the computation complexity and then the resulting patterns are merged step by step into large-size biclusters incorporated with gene functional modules. The algorithm integrates the numerical characteristics in a gene expression matrix and the gene functions in the biological activities. Our experiments on real data show that the new algorithm outperforms most existing methods for mining gene functional biclusters.