A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

Abstract

BackgroundIn a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed.MethodsWe introduce BiMine, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, BiMine relies on a new evaluation function called Average Spearman's rho (ASR). Second, BiMine uses a new tree structure, called Bicluster Enumeration Tree (BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, BiMine introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters.ResultsThe performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that BiMine competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.

Scatter search-based identification of local patterns with positive and negative correlations in gene expression data

“…Recently, several biclustering algorithms using measures based on correlations have been proposed to obtain co-expressed genes [40][41][42][43][44][45][46]. In particular, BCCA [44] is a nondeterministic greedy algorithm which builds an initial bicluster composed of a pair of genes by removing experimental conditions while the Pearson correlation coefficient is lesser than a given threshold, and later, all the genes maintaining the correlation are added to this bicluster.…”

Differential biclustering for gene expression analysis

“…There are several ways that have been proposed to create a new representation of the gene expression data; such as ranking the expression values of each gene [1] and representing the gene expression data with two values, 1 and 2.…”

Noise-robust algorithm for identifying functionally associated biclusters from gene expression data

“…Also, reg-Cluster constructs a DFS tree, in the same manner as Tri-Cluster, which leads to the same problems mentioned above. The BiMine [5] mines sub-matrices for which the evaluation function called ASR (Average Correlation Value) is above a threshold. It constructs a DFS tree for which the node represents a submatrix.…”