Mining multiple phenotype structures underlying gene expression profiles

ABSTRACT

DNA microarray technology is now widely used in basic biomedical research for mRNA expression profiling and are increasingly being used to explore patterns of gene expression in clinical research. Automatically detecting phenotype structures from gene expression profiles can provide deep insight into the nature of many diseases as well as lead in the development of new drugs. While most of the previous studies focus on only mining empirical phenotype structure which the experiment controls, it is also interesting to detect possible hidden phenotype structures underlying gene expression profiles.Since the number of samples is usually limited, such data sets are very sparse in high-dimensional gene space. Furthermore, most of the genes of interest are buried in large amount of noise. Unsupervised phenotype structure discovery of such sparse high-dimensional data sets present interesting but challenging problems. In this paper, we propose the model of simultaneously mining both empirical and hidden phenotype structures from gene expression data. We demonstrate the effectiveness and efficiency of the proposed method on various real-world data sets.

References

1. Agrawal, R., Gehrke, J., Gunopulos, D., and Raghavan, P. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, pages 94--105, 1998.
2. Alon U., Barkai N., Notterman D. A., Gish K., Ybarra S., Mack D. and Levine A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide array. Proc. Natl. Acad. Sci. USA, Vol. 96(12):6745--6750, June 1999.
3. Barash Y. and Friedman N. Context-specific bayesian clustering for gene expression data. In Proc. 5th Annual International Conference on Computational Molecular Biology (RECOMB), pages 12--20. ACM Press, 2001.
4. Ben-Dor A., Shamir R. and Yakhini Z. Clustering gene expression patterns. Journal of Computational Biology, 6(3/4):281--297, 1999.
5. Brown M. P. S., Grundy W. N., Lin D., Cristianini N., Sugnet C. W., Furey T. S., Ares M. Jr. and Haussler D. Knowledge-based analysis of microarray gene expression data using support vector machines. Proc. Natl. Acad. Sci., 97(1):262--267, January 2000.
6. Cheng Y., Church GM. Biclustering of expression data. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (ISMB), 8:93--103, 2000.
7. Ding, Chris. Analysis of gene expression profiles: class discovery and leaf ordering. In Proc. of International Conference on Computational Molecular Biology (RECOMB), pages 127--136, Washington, DC., April 2002.
8. Eisen M. B., Spellman P. T., Brown P. O. and Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, Vol. 95:14863--14868, 1998.
9. Golub T. R., Slonim D. K. et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, Vol. 286(15):531--537, October 1999.
10. Hedenfalk, I., Duggan, D., Chen, Y. D., Radmacher, M., Bittner, M., Simon, R., Meltzer, P., Gusterson, B., Esteller, M., Kallioniemi, O. P., Wilfond, B., Borg, A., and Trent, J. Gene-expression profiles in hereditary breast cancer. The New England Journal of Medicine, 344(8):539--548, February 2001.
11. Kirkpatrick, S., Gelatt, C. D. Jr., andVecchi, M. P. Optimization by simulated annealing. Science, 220(4598):671--680, 1983.
12. Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander. OPTICS: Ordering Points To Identify the Clustering Structure. Sigmod, pages 49--60, 1999.
13. Ng, Raymond T. and Han, Jiawei. Clarans: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering, 14(5):1003--1016, October 2002.
14. Peterson Leif E. Factor analysis of cluster-specific gene expression levels from cdna microarrays. Computer Methods and Programs in Biomedicine, 69(3):179--188, 2002.
15. Rand, W. M. Objective criteria for evaluation of clustering methods. Journal of the American Statistical Association, 1971.
16. Rhodes, D. R., Miller, J. C., Haab, B. B., Furge, K. A. CIT: Identification of Differentially Expressed Clusters of Genes from Microarray Data. Bioinformatics, 18:205--206, 2001.
17. Schloegel, Kirk, Karypis, George. CRPC Parallel Computing Handbook, chapter Graph Partitioning For High Performance Scientific Simulations. Morgan Kaufmann, 2000.
18. Shamir R. and Sharan R. Click: A clustering algorithm for gene expression analysis. In In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMB '00). AAAI Press., 2000.
19. Tang, Chun and Zhang, Aidong. An iterative strategy for pattern discovery in high-dimensional data sets. In Proceeding of 11th International Conference on Information and Knowledge Management (CIKM 02), McLean, VA, November 4-9 2002.
20. Thomas J. G., Olson J. M., Tapscott S. J. and Zhao L. P. An Efficient and Robust Statistical Modeling Approach to Discover Differentially Expressed Genes Using Genomic Expression Profiles. Genome Research, 11(7):1227--1236, 2001.
21. Xing E. P. and Karp R. M. Cliff: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics, Vol. 17(1):306--315, 2001.
22. Xu, Ying, Olman, Victor and Xu, Dong. Clustering gene expression data using a graph-theoretic approach: An application of minimum spanning trees. Bioinformatics, 18(4):536--545, 2002.
23. Yang, Jiong, Wang, Wei, Wang, Haixun and Yu, Philip S. δ-cluster: Capturing Subspace Correlation in a Large Data Set. In Proceedings of 18th International Conference on Data Engineering (ICDE 2002), pages 517--528, 2002.
24. Yeung, Ka Yee and Ruzzo, Walter L. An empirical study on principal component analysis for clustering gene expression data. Technical Report UW-CSE-2000-11-03, Department of Computer Science & Engineering, University of Washington, 2000.
25. Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E., Ruzzo, W. L. Model-based clustering and data transformations for gene expression data. Bioinformatics, 17:977--987, 2001.

Index Terms

• Information systems

  • Information systems applications

    • Data mining