Extracting Pseudo-Biclusters from Gene Expression Data Based on Suffix Array

Abstract

We discuss in this paper a method for finding Pseudo-Biclusters of gene expression data. For time series data, a linear time algorithm with the help of suffix tree has been proposed. Although the algorithm can efficiently enumerate all maximal biclusters, we often observe many overlapping clusters. By combining such clusters together, we can interestingly observe that all genes in the combined cluster behaves quite similarly within a common time span, but they behaves differently after that. We expect that such an observation would provide valuable suggetions to experts. From this point of view, we introduce a notion of pseudo-biclusters. A pseudo-bicluster consists of several maximal biclusters with some overlap. We design a polynomial time algorithm for finding them with a suffix tree. Some experimental results for gene expression data of ascidian (Hoya) are also presented, showing an interesting cluster actually extracted.