A novel biclustering approach with iterative optimization to analyze gene expression data

Abstract

Sawannee Sutheeworapong,1,2 Motonori Ota,4 Hiroyuki Ohta,1 Kengo Kinoshita2,31Department of Biological Sciences, Graduate School of Biosciences and Biotechnology, Tokyo Institute of Technology, Tokyo, Japan; 2Graduate School of Information Sciences, 3Institute of Development, Aging and Cancer, Tohoku University, Miyagi, Japan; 4Graduate School of Information Sciences, Nagoya University, Nagoya, JapanObjective: With the dramatic increase in microarray data, biclustering has become a promising tool for gene expression analysis. Biclustering has been proven to be superior over clustering in identifying multifunctional genes and searching for co-expressed genes under a few specific conditions; that is, a subgroup of all conditions. Biclustering based on a genetic algorithm (GA) has shown better performance than greedy algorithms, but the overlap state for biclusters must be treated more systematically.Results: We developed a new biclustering algorithm (binary-iterative genetic algorithm [BIGA]), based on an iterative GA, by introducing a novel, ternary-digit chromosome encoding function. BIGA searches for a set of biclusters by iterative binary divisions that allow the overlap state to be explicitly considered. In addition, the average of the Pearson’s correlation coefficient was employed to measure the relationship of genes within a bicluster, instead of the mean square residual, the popular classical index. As compared to the six existing algorithms, BIGA found highly correlated biclusters, with large gene coverage and reasonable gene overlap. The gene ontology (GO) enrichment showed that most of the biclusters are significant, with at least one GO term over represented.Conclusion: BIGA is a powerful tool to analyze large amounts of gene expression data, and will facilitate the elucidation of the underlying functional mechanisms in living organisms.Keywords: biclustering, microarray data, genetic algorithm, Pearson’s correlation coefficient