Efficient mining differential co-expression biclusters in microarray datasets

Background: Biclustering 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. Methods: In 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. Results: The 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. Conclusions: Our proposed algorithm can find more statistically significant and biological biclusters in two microarray datasets than the other two algorithms.