- Meeting abstract
- Open Access
Exact biclustering algorithm for the analysis of large gene expression data sets
© Voggenreiter et al; licensee BioMed Central Ltd. 2012
- Published: 14 December 2012
- Gene Expression Data
- Bipartite Graph
- Search Tree
- Maximal Clique
- Clique Enumeration
Biclustering of gene expression data is used to discover groups of genes that are co-expressed over a subset of tested conditions. The objective is to maximize the detection of significant biclusters; to do so, most approaches employ a heuristic approximation in order to avoid a non-polynomial computational complexity.
Previous algorithms have focused on enabling the discovery of biologically relevant results within the scope of single studies, where data size and complexity are limited. New methods and algorithms are required in order to enable applications of biclustering to larger scale data sets that can span multiple experiments and that are potentially far more heterogenous.
The BiMax  algorithm uses a binary representation of the gene expression matrix that has been proven to discover enriched modules of biologically relevant genes in gene expression data. This model of biclustering allows for exact solutions, however, the BiMax algorithm performs best on a restricted size of input data. We can view the biclustering formulation of BiMax as the search for all maximal bicliques in a bipartite graph; where the nodes are genes or experiments and a connection between a gene and an experiment exists if the gene was significantly expressed in that experiment. We propose a new algorithm capable of enumerating all biclusters on such a graph. In order to solve the maximal biclique enumeration problem, we make use of the backtracking Bron-Kerbosch algorithm  for maximal clique enumeration. We have developed and successfully tested a new algorithm, the Bipartite Bron-Kerbosch algorithm, which uses similar principles to Bron-Kerbosch but traverses the bicliques on bipartite graphs. This approach enables the algorithm to explore all maximal bicliques without visiting branches of the search tree that contain previously discovered biclusters.
BiMax vs. Bipartite Bron-Kerbosch Running Times. Running times of the Bipartite Bron-Kerbosch (BBK) algorithm compared to BiMax on binary matrices derived from A. Thaliana gene expression data. Each matrix had a density of around 12% and the algorithms were given a maximum of 1 hour to complete on the same computer. The number of biclusters in each matrix is listed in the last column.
- Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 2006, 22(9):1122–1129. 10.1093/bioinformatics/btl060View ArticlePubMedGoogle Scholar
- Bron C, Kerbosch J: Finding All Cliques of an Undirected Graph. Communications of the ACM 1973, 16(9):3.View ArticleGoogle Scholar
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