Efficient mining differential co-expression biclusters in microarray datasets

BackgroundBiclustering algorithm can find a number of co-expressed genes under a set of experimental conditions. Recently, differential co-expression bicluster mining has been used to infer the reasonable patterns in two microarray datasets, such as, normal and cancer cells.MethodsIn this paper, we propose an algorithm, DECluster, to mine Differential co-Expression biCluster in two discretized microarray datasets. Firstly, DECluster produces the differential co-expressed genes from each pair of samples in two microarray datasets, and constructs a differential weighted undirected sample-sample relational graph. Secondly, the differential biclusters are generated in the above differential weighted undirected sample-sample relational graph. In order to mine maximal differential co-expression biclusters efficiently, we design several pruning techniques for generating maximal biclusters without candidate maintenance.ResultsThe experimental results show that our algorithm is more efficient than existing methods. The performance of DECluster is evaluated by empirical p-value and gene ontology, the results show that our algorithm can find more statistically significant and biological differential co-expression biclusters than other algorithms.ConclusionsOur proposed algorithm can find more statistically significant and biological biclusters in two microarray datasets than the other two algorithms.

► DECluster algorithm can identify new types of DC biclusters. ► DECluster can mine maximal DC biclusters without candidate maintenance. ► DECluster can find more statistically significant and biological biclusters.