Iterative Clustering Algorithm for Analyzing Temporal Patterns of Gene Expression
Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms.
[1] P.O. Brown, and D. Botstein, "Exploring the new world of the genome
with DNA microarrays", The chipping forecast, vol. 21, 1999. pp. 33-37.
[2] M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein, "Cluster
analysis and display of genome-wide expression patterns", Proceeding of
the National Academy of Sciences, vol. 95, 1998, pp. 14863-14868.
[3] P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M.B.
Eisen, P.O. Brown, D. Botstein, and B. Futcher, "Comprehensive
identification of cell cycle-regulated genes of the yest Saccharomyces
cerevisiae by microarray hydridization", Mol. Biol. Cell, vol. 9, pp.
3273-3279.
[4] J.L. DeRisi, V.R. Iyer, and P.O. Brown, "Exploring the metabolic and
genetic control of gene expression on a genomic scale", Science, vol. 278,
1997, pp. 680-686.
[5] S. Chu, and J.L. DeRisi et al., "The transcriptional program of sporulation
in budding yeast", Science, vol. 282, 1998, pp. 699-705
[6] R.J. Cho, et al., "A genome-wide transcriptional analysis of the mitotic
cell cycle", Mol. Cell., vol. 2, 1998, pp. 65-73.
[7] M.J.L. De Hoon, S. Imoto, and S. Miyano, "Statistical analysis of a small
set of time-ordered gene expresion data using linear splines",
Bioinformatics, vol. 18, 2002, pp. 1477-1485.
[8] Y. Luan, and H. Li, "Clustering of time-course gene expression data using
a mixed-effects model with B-splines", Bioinformatics, vol. 19, 2003, pp.
474-482.
[9] S.D. Peddada, E.K. Lobenhofer, L. Li, et al., "Gene selection and
clustering for time-course and dose-response microarray experiments
using order-restricted inference", Bioinformatics, vol. 19, 2003. pp.
834-841.
[10] S. Datta, and S. Datta, "Comparisons and validation of statistical
clustering techniques for microarray gene expression data",
Bioinformatics, vol. 19, 2003, pp. 459-466.
[11] A. Bhattacharjee, W.G. Richards, and J. Staunton, J. et al., "Classification
of human lung carcinomas by mRNA expression profiling reveals distinct
adenocarcinomas sub-class", Proceeding of the National Academy of
Sciences, vol. 98, 2001, pp. 13790-13795
[12] S. Dudoit, J. Fridlyand, "A prediction-based resampling method for
estimating the number of clusters in a dataset", Genome Biology, vol. 3,
2002, research0036.1-0036.21.
[13] A.K. Jain, and J. Moreau, "Bootstrap techniques in cluster analysis",
Pattern Recognition, vol. 20, 1988, pp. 547-568.
[14] E. Levine, and E. Domany, "Resampling method for unsupervised
estimation of cluster validity", Neural Computation, vol. 13, 2001, pp.
2573-2593.
[15] S. Monti, P. Tamayo, J. Mesirov, and T. Golub, "Consensus Clustering: A
resampling based method for class discovery and visualization of gene
expression microarray data", Kluwer Academic Publishers, 2003.
[16] S.Y. Kim, J.W. Lee, and T.M. Choi, "Ensemble clustering method based
on the resampling similarity measure for gene expression data", 2004,
Submitted.
[17] L. Huber, and P. Arabie, "Comparing partitions", Journal of
Classification, vol. 2, 1985, pp. 193-218.
[18] K.Y. Yeung, and W.L. Ruzzo, "An empirical study on principal
component analysis for clustering gene expression data", Technical
Report 2000 UW-CSE-00-11-01, Department of Computer Science and
Engineering, University of Washington.
[19] http://smgm.stanford.edu/pbrown/sporulation.
[20] J. Quackenbush, "Computional analysis of microarray expression data.",
Bioinformatics, vol. 18, 2001, pp. 1-10.
[1] P.O. Brown, and D. Botstein, "Exploring the new world of the genome
with DNA microarrays", The chipping forecast, vol. 21, 1999. pp. 33-37.
[2] M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein, "Cluster
analysis and display of genome-wide expression patterns", Proceeding of
the National Academy of Sciences, vol. 95, 1998, pp. 14863-14868.
[3] P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M.B.
Eisen, P.O. Brown, D. Botstein, and B. Futcher, "Comprehensive
identification of cell cycle-regulated genes of the yest Saccharomyces
cerevisiae by microarray hydridization", Mol. Biol. Cell, vol. 9, pp.
3273-3279.
[4] J.L. DeRisi, V.R. Iyer, and P.O. Brown, "Exploring the metabolic and
genetic control of gene expression on a genomic scale", Science, vol. 278,
1997, pp. 680-686.
[5] S. Chu, and J.L. DeRisi et al., "The transcriptional program of sporulation
in budding yeast", Science, vol. 282, 1998, pp. 699-705
[6] R.J. Cho, et al., "A genome-wide transcriptional analysis of the mitotic
cell cycle", Mol. Cell., vol. 2, 1998, pp. 65-73.
[7] M.J.L. De Hoon, S. Imoto, and S. Miyano, "Statistical analysis of a small
set of time-ordered gene expresion data using linear splines",
Bioinformatics, vol. 18, 2002, pp. 1477-1485.
[8] Y. Luan, and H. Li, "Clustering of time-course gene expression data using
a mixed-effects model with B-splines", Bioinformatics, vol. 19, 2003, pp.
474-482.
[9] S.D. Peddada, E.K. Lobenhofer, L. Li, et al., "Gene selection and
clustering for time-course and dose-response microarray experiments
using order-restricted inference", Bioinformatics, vol. 19, 2003. pp.
834-841.
[10] S. Datta, and S. Datta, "Comparisons and validation of statistical
clustering techniques for microarray gene expression data",
Bioinformatics, vol. 19, 2003, pp. 459-466.
[11] A. Bhattacharjee, W.G. Richards, and J. Staunton, J. et al., "Classification
of human lung carcinomas by mRNA expression profiling reveals distinct
adenocarcinomas sub-class", Proceeding of the National Academy of
Sciences, vol. 98, 2001, pp. 13790-13795
[12] S. Dudoit, J. Fridlyand, "A prediction-based resampling method for
estimating the number of clusters in a dataset", Genome Biology, vol. 3,
2002, research0036.1-0036.21.
[13] A.K. Jain, and J. Moreau, "Bootstrap techniques in cluster analysis",
Pattern Recognition, vol. 20, 1988, pp. 547-568.
[14] E. Levine, and E. Domany, "Resampling method for unsupervised
estimation of cluster validity", Neural Computation, vol. 13, 2001, pp.
2573-2593.
[15] S. Monti, P. Tamayo, J. Mesirov, and T. Golub, "Consensus Clustering: A
resampling based method for class discovery and visualization of gene
expression microarray data", Kluwer Academic Publishers, 2003.
[16] S.Y. Kim, J.W. Lee, and T.M. Choi, "Ensemble clustering method based
on the resampling similarity measure for gene expression data", 2004,
Submitted.
[17] L. Huber, and P. Arabie, "Comparing partitions", Journal of
Classification, vol. 2, 1985, pp. 193-218.
[18] K.Y. Yeung, and W.L. Ruzzo, "An empirical study on principal
component analysis for clustering gene expression data", Technical
Report 2000 UW-CSE-00-11-01, Department of Computer Science and
Engineering, University of Washington.
[19] http://smgm.stanford.edu/pbrown/sporulation.
[20] J. Quackenbush, "Computional analysis of microarray expression data.",
Bioinformatics, vol. 18, 2001, pp. 1-10.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:55883", author = "Seo Young Kim and Jae Won Lee and Jong Sung Bae", title = "Iterative Clustering Algorithm for Analyzing Temporal Patterns of Gene Expression ", abstract = "Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms. ", keywords = "Clustering, microarray experiment, temporal
pattern of gene expression data.", volume = "1", number = "4", pages = "215-4", }
