Ccc-bicluster Analysis for Time Series Gene Expression Data

Many of the biclustering problems have been shown to be NP-complete. However, when they are interested in identify biclusters in time series expression data, it can limit the problem by finding only maximal biclusters with contiguous columns. This restriction leads to a well-mannered problem. Its motivation is the fact that biological processes start and conclude in an identifiable contiguous period of time, leading to increased (or decreased) activity of sets of genes forming biclusters with adjacent columns. In this context, propose an algorithm that finds and reports all maximal adjacent column coherent biclusters (CCC-Biclusters), in time linear in the size of the expression matrix. Each relevant CCC-Bicluster identified corresponds to the detection of a coherent expression pattern shared by a group of genes in a adjacent subset of time-points and identifies a potentially relevant regulatory module. The linear time complexity of CCC-Biclustering is acquired by manipulating a discretized version of the gene expression matrix and using efficient string dealing out techniques based on suffix trees. The effectiveness was obtained by applying the algorithm to the transcriptomic expression patterns stirring in Saccharomyces cerevisiae in response to heat stress. Key Words— Biclustering, gene expression data,