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Consensus Clustering for Time Course Gene Expression Microarray Data
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 Title & Authors
Consensus Clustering for Time Course Gene Expression Microarray Data
Kim, Seo-Young; Bae, Jong-Sung;
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 Abstract
The rapid development of microarray technologies enabled the monitoring of expression levels of thousands of genes simultaneously. Recently, the time course gene expression data are often measured to study dynamic biological systems and gene regulatory networks. For the data, biologists are attempting to group genes based on the temporal pattern of their expression levels. We apply the consensus clustering algorithm to a time course gene expression data in order to infer statistically meaningful information from the measurements. We evaluate each of consensus clustering and existing clustering methods with various validation measures. In this paper, we consider hierarchical clustering and Diana of existing methods, and consensus clustering with hierarchical clustering, Diana and mixed hierachical and Diana methods and evaluate their performances on a real micro array data set and two simulated data sets.
 Keywords
Consensus clustering;Time course gene expression data;Validation measures;
 Language
Korean
 Cited by
 References
1.
Bhattacharjee, A., Richards, W.G., Staunton, J., Li, C., Monti, S., Vasa, P., Ladd, C., Beheshti, J., Bueno, B., Gillette, M., Loda, M., Weber, G., Mark, E.J., Lander, E.S., Wong, W., Johnson, B.E., Golub, T.R., Sugarbaker, D.J., and Meyerson, M. (2001). Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinomas sub-class. Proceeding of the National Academy of Sciences, 98, 13790-13795

2.
Brown, P.O., Botstein, D. (1999). Exploring the new world of the genome with DNA microarrays. The chipping forecast, 21, 33-37

3.
Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J. and Davis, R.W. (1998). A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell, 2, 65-73 crossref(new window)

4.
Chu, S., DeRisi, J., Eisen, M., Mulholland, J., Botstein, D., Brown, P.O. and Herskowitz, I. (1998). The transcriptional program of sporulation in budding yeast. Science, 282, 699-705 crossref(new window)

5.
Datta. S. and Datta, S. (2003). Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics, 19, 459-466 crossref(new window)

6.
De Hoon, M.J.L., lmoto, S. and Miyano, S. (2002). Statistical analysis of a small set of time-ordered gene expresion data using linear splines. Bioinformatics, 18, 1477-1485 crossref(new window)

7.
DeRisi, J.L., Iyer, V.R. and Brown, P.O. (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278, 680-686 crossref(new window)

8.
Dudoit, S., and Fridlyand, J. (2002). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Bilogy, 3, research0036

9.
Eisen, M.B., Spellman, P.T., Brown, P.O. and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceeding of the National Academy of Sciences, 95, 14863-14868

10.
Huber, L. and Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193-218 crossref(new window)

11.
Jain, A.K. and Moreau, J. (1988). Bootstrap techniques in cluster analysis. Pattern Recognition, 20, 547-568

12.
Kim, S. Y. and Lee, J. W. (2004). Ensemble clustering method based on the resampling similarity measure for gene expression data. Submitted

13.
Levine, E. and Domany, E. (2001). Resampling method for unsupervised estimation of cluster validity. Neural Computation, 13, 2573-2593 crossref(new window)

14.
Luan, Y. and Li, H. (2003). Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics, 19, 474-482 crossref(new window)

15.
Monti, S., Tamayo, P., Mesirov, J. and Golub, T. (2003). Consensus Clustering: A resampling based method for class discovery and visualization of gene expression microarray data. Kluwer Academic Publishers

16.
Peddada, S.D., Lobenhofer, E.K., Li, L., Afshari, C.A., Weinberg, C.R. and Umbach, D.M. (2003). Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics, 19, 834-841 crossref(new window)

17.
Quackenbush, J. (2001). Computional analysis of micro array data. Nature Reviews, Genetics, 2, 418-427 crossref(new window)

18.
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D. and Futcher, B. (1998). Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273-3297 crossref(new window)