Detection of Significant Biclusters in Gene Expression Data using Reactive Greedy Randomized Adaptive Search Algorithm

Abstract

In microarray gene expression data, a bicluster is a subset of genes which exhibit similar expression patterns along a subset of conditions. Each bicluster is represented as a tightly co-regulated submatrix of the gene expression matrix. One of the most popular measures to evaluate the quality of a bicluster is the mean squared residue score. Our approach aims to detect significant biclusters from a large microarray dataset through a metaheuristic algorithm called reactive greedy randomized adaptive search procedure (R-GRASP). The method finds biclusters by starting from small tightly co-regulated bicluster seeds and iteratively adds more genes and conditions to it while keeping the mean squared residue score below a predetermined threshold. In R-GRASP, the parameter that defines the blend of greediness and randomness is self adjustable depending on the quality of solutions found previously. We performed statistical validation of the biclusters obtained through p-value calculation and evaluated the results against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results on the Yeast gene expression data indicate that the Reactive GRASP outperforms the basic GRASP and the Cheng and Church approach.