A Linear Time Biclustering Algorithm for Time Series Gene Expression Data
Several non-supervised machine learning methods have been used in the analysis of gene expression data obtained from microarray experiments. Recently, biclustering, a non-supervised approach that performs simultaneous clustering on the row and column dimensions of the data matrix, has been shown to be remarkably effective in a variety of applications. The goal of biclustering is to find subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated behaviors. In the most common settings, biclustering is an NP-complete problem, and heuristic approaches are used to obtain sub-optimal solutions using reasonable computational resources.
In this work, we examine a particular setting of the problem, where we are concerned with finding biclusters in time series expression data. In this context, we are interested in finding biclusters with consecutive columns. For this particular version of the problem, we propose an algorithm that finds and reports all relevant biclusters in time linear on the size of the data matrix. This complexity is obtained by manipulating a discretized version of the matrix and by using string processing techniques based on suffix trees. We report results in both synthetic and real data that show the effectiveness of the approach.
KeywordsGene Expression Data Internal Node Linear Time Algorithm Gene Expression Matrix Path Label
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- 1.Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z.: Discovering local structure in gene expression data: The order–preserving submatrix problem. In: Proc. of the 6th International Conference on Computacional Biology, pp. 49–57 (2002)Google Scholar
- 2.Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proc. of the 8th International Conference on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)Google Scholar
- 5.Koyuturk, M., Szpankowski, W., Grama, A.: Biclustering gene-feature matrices for statistically significant dense patterns. In: Proc. of the 8th Annual International Conference on Research in Computational Molecular Biology, pp. 480–484 (2004)Google Scholar
- 6.Liu, J., Wang, W., Yang, J.: Biclustering in gene expression data by tendency. In: Proc. of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, pp. 182–193 (2004)Google Scholar
- 10.Martin, D., Brun, C., Remy, E., Mouren, P., Thieffry, D., Jacq, B.: Gotoolbox: functional investigation of gene datasets based on gene ontology. Genome Biology 5(12), R101 (2004)Google Scholar
- 12.Monteiro, P., Teixeira, M.C., Jain, P., Tenreiro, S., Fernandes, A.R., Mira, N., Alenquer, M., Freitas, A.T., Oliveira, A.L., Sá-Correia, I.: Yeast search for transcriptional regulators and consensus tracking (yeastract) (2005), http://www.yeastract.com
- 13.Murali, T.M., Kasif, S.: Extracting conserved gene expression motifs from gene expression data. In: Proc. of the Pacific Symposium on Biocomputing, vol. 8, pp. 77–88 (2003)Google Scholar
- 15.Sheng, Q., Moreau, Y., De Moor, B.: Biclustering microarray data by Gibbs sampling. Bioinformatics 19(Suppl. 2), 196–205 (2003)Google Scholar
- 16.Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(Suppl. 1), S136–S144 (2002)Google Scholar