Biclustering Expression Data Using Node Addition Algorithm

Abstract

Biclustering algorithms simultaneously cluster both rows and columns. This type of algorithms are applied to gene expression data analysis to find a subset of genes that exhibit similar expression pattern under a subset of conditions. Cheng and Church introduced the mean squared residue measure to capture the coherence of a subset of genes over a subset of conditions. They provided a set of heuristic algorithms based primarily on node deletion to find one bicluster or a set of biclusters after masking discovered biclusters with random values. Masking of discovered biclusters with random values interferes with discovery of high quality biclusters. We provide an efficient node addition algorithm to find a set of biclusters without the need of masking discovered biclusters. Initialized with a gene and a subset of conditions, a bicluster is extended by adding more genes and conditions. Thus it provides facility to study individual genes, besides generating a large number of biclusters with different initializations. Biclusters with lower or higher scores within a specified limit can be generated by parameter setting. Use of incremental method of computing score makes the algorithm faster.