On Biclustering of Gene Expression Data

Microarray technology enables the monitoring of the expression patterns of a huge number of genes across different experimental conditions or time points simultaneously. Biclustering of microarray data is an important technique to discover a group of genes that are co-regulated in a subset of experimental conditions. Traditional clustering algorithms find groups of genes/conditions over the complete feature space. Therefore they may fail to discover the local patterns where a subset of genes has similar behaviour over a subset of conditions. Biclustering algorithms aim to discover such local patterns from the gene expression matrix, thus can be thought as simultaneous clustering of genes and conditions. In recent years, a large number of biclustering algorithms have been proposed in literature. In this article, a study has been made on various issues regarding the biclustering problem along with a comprehensive survey on available biclustering algorithms. Moreover, a survey on freely available biclustering software is also made.