Ensemble biclustering gene expression data based on the spectral clustering

Many biclustering algorithms and bicluster criteria have been proposed in analyzing the gene expression data. However, there are no clues about the choice of a specific biclustering algorithm, which make ensemble biclustering method receive much attention for aggregating the advantage of various biclustering algorithms. Although the method of co-association consensus (COAC) is a landmark of ensemble biclustering, the effectiveness and efficiency are the worst in state-of-the-art methods. In this paper, to improve COAC, we propose spectral ensemble biclustering (SEB) in which an novel method for generating a set of basic biclusters is proposed for generating the basic biclusters with better quality as well as higher diversity and an new consensus method is also adopted for combing the above basic biclusters. In SEB, spectral clustering is directly applied to the co-association matrix and equivalently transformed into the weighted k-means. Experiments on six gene expression data demonstrate that the effectiveness, efficiency and scalability of SEB are the best compared with existing ensemble methods in terms of the biological significance and runtime.