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Gene Screening and Clustering of Yeast Microarray Gene Expression Data
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 Title & Authors
Gene Screening and Clustering of Yeast Microarray Gene Expression Data
Lee, Kyung-A; Kim, Tae-Houn; Kim, Jae-Hee;
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 Abstract
We accomplish clustering analyses for yeast cell cycle microarray expression data. To reflect the characteristics of a time-course data, we screen the genes using the test statistics with Fourier coefficients applying a FDR procedure. We compare the results done by model-based clustering, K-means, PAM, SOM, hierarchical Ward method and Fuzzy method with the yeast data. As the validity measure for clustering results, connectivity, Dunn index and silhouette values are computed and compared. A biological interpretation with GO analysis is also included.
 Keywords
Connectivity;Dunn index;Fourier coefficient;FDR;Fuzzy;Microarray expression data;Model-based clustering;K-means;PAM;Silhouette;SOM;Ward method;yeast;
 Language
Korean
 Cited by
1.
효모 마이크로어레이 유전자 발현데이터에 대한 가우시안 과정 회귀를 이용한 유전자 선별 및 군집화,김재희;김태훈;

응용통계연구, 2013. vol.26. 3, pp.389-399 crossref(new window)
1.
Screening and Clustering for Time-course Yeast Microarray Gene Expression Data using Gaussian Process Regression, Korean Journal of Applied Statistics, 2013, 26, 3, 389  crossref(new windwow)
 References
1.
김재희 (2011). R 다변량 통계 분석, 교우사, 서울

2.
김재희, 고윤실 (2009). 군집분석 비교 및 한우 관능평가데이터 군집화, 응용통계 연구, 22, 745-758. crossref(new window)

3.
Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal of the Royal Statistical Society: Series B, 57, 289-300.

4.
Bickel, D. R. (2011). Estimating the null distribution to adjust observed confidence levels for genome-scale screening, Bioinformatics, 67, 363-370.

5.
Datta, S. and Datta, S. (2005). Empirical Bayes screening of many p-values with application to microarray studies, Bioinformatics, 21, 1987-1994. crossref(new window)

6.
Dudoit, S., Shaffer, J. P. and Boldrick, J. C. (2003). Multiple hypothesis testing in microarray experiments, Statistical Science, 18, 71-103. crossref(new window)

7.
Dunn (1974). Well-separated clusters and optimal fuzzy partitions, Journal of Cybernetics, 4, 95-104. crossref(new window)

8.
Eckel, J. E., Gennings, C., Chinchilli, V. M., Burgoon, L. D. and Zacharewski, T. R. (2004). Empirical Bayes gene screening tool for time-course or dose-response microarray data, Journal of Biopharmaceutical Statistics, 14, 647-670. crossref(new window)

9.
Fraley, C. and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation, Journal of the American Statistical Association, 97, 611-631. crossref(new window)

10.
Fraley, C. and Raftery, A. E. (2006). MCLUST Version 3 for R: Normal mixture modeling and model-based clustering, Technical Report No. 504.

11.
Gentleman, R., Caray, V. J., Huber, W., Irizarry, R. A. and Dudoit, S. (2005). Bioinformatics and computational biology solutions using R and bioconductor, Spinger, New York.

12.
Getz, G., Levine, E., Domany, E. and Zhang, M. Q. (2000). Super-paramagneic clustering of yeast expression profiles. Physica, A279, 457-464.

13.
Handl, J., Knowles, J. and Kell, D. B. (2005). Computational cluster validation in post-genomic data analysis. Bioinformatics, 21, 3201-3212. crossref(new window)

14.
Huang, D. W., Sherman, B. T. and Lempicki, R. A.(2009). Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources, Nature Protocols, 4, 44-57.

15.
Hero, A. O., Fleury, G., Mears, A. J. and Swaroop, A. (2004). Multicriteria gene screening for analysis of differential expression with DNA microarrays, Journal on Applied Signal processing, 2004, 43-52. crossref(new window)

16.
Izenman, A. J. (2008). Modern Multivariate Statistical Techniques, Spinger, New York.

17.
Kim, B. R., Littell, R. C. and Wu, R. (2006). Clustering periodic patterns of gene expression based on fourier appoximations, Current Genomics, 7, 197-203. crossref(new window)

18.
Kim, J. and Hart, J. D. (1998). Test for change when the data are dependent, Journal of Time Series, 19, 399-424. crossref(new window)

19.
Kim, J. and Kim, H. (2008). Clustering of change using Fourier coefficient, Bioinformatics, 24, 184-191. crossref(new window)

20.
Kim, J., Ogden, R. T. and Kim, H. (2011). A method of identify differential expression profile with timecourse gene data and Fourier transformation, BMC Bioinformatics, in revision.

21.
Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York.

22.
Kohonen, T. (1998). The self-organizing map, Neurocomputing, 21, 1-6. crossref(new window)

23.
Ma, S. (2006). Empirical study of supervised gene screening, BMC Bioinformatics, 7, 537. crossref(new window)

24.
Rousseeuw, P. T. (1987). Silhouettes: Graphical aid to the interpretation and validation of cluster analysis, Journal of Computation Applied Math, 20, 53-65. crossref(new window)

25.
Serban, N. and Wasserman, L. (2005). CATS: Clustering after transformation and smoothing, Journal of the American Statistical Association, 471, 990-999.

26.
Tusher, V. G., Tibshirani, R. and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, Proceedings of the National Academy of Sciences of the United States of America, 98, 5116-5121. crossref(new window)

27.
Toronen, R., Kolehmainen, M., Wong, G. and Castren, E. (1999). Analysis of gene expression data using self-organizing maps, Federation of European Biochemical Societies, 451, 142-146. crossref(new window)

28.
Zhang, L., Zhang, A. and Ramanathan, M. (2003). Fourier harmonic approach for visualizing temporal patterns of gene expression data, IEEE Computer Society Bioinformatics Conference, 2, 137-147.