Application of Density Based Clustering to Microarray Data Analysis

• Autorzy: Raczynski L. , Wozniak K. , Rubel T. , Zaremba K.
• Języki publikacji: EN

Abstrakty

EN

In just a few years, gene expression microarrays have rapidly become a standard experimental tool in the biological and medical research. Microarray experiments are being increasingly carried out to address the wide range of problems, including the cluster analysis. The estimation of the number of clusters in datasets is one of the main problems of clustering microarrays. As a supplement to the existing methods we suggest the use of a density based clustering technique DBSCAN that automatically defines the number of clusters. The DBSCAN and other existing methods were compared using the microarray data from two datasets used for diagnosis of leukemia and lung cancer.

Słowa kluczowe

EN

microarrays; cluster analysis; DBSCAN

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2010
• Tom: Vol. 56, No. 3
• Strony: 281--286
• Opis fizyczny: Bibliogr. 14 poz., wykr.

Twórcy

autor

Raczynski L.

autor

Wozniak K.

autor

Rubel T.

autor

Zaremba K.

• Institute of Radioelectronics, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland,

Bibliografia

• [1] A. Bhattacharjee, W. G. Richards, J. Staunton, C. Li, S. Monti, P. Vasa, C. Ladd, J. Beheshti, R. Bueno, M. Gillette, M. Loda, G. Weber, E. J. Mark, E. S. Lander, W. Wong, B. E. Johnson, T. R. Golub, D. J. Sugarbaker, and M. Meyerson, “Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses,” Proc. Natl. Acad. Sci. U.S.A., vol. 98, pp. 13 790-13 795, Nov 2001.
• [2] S. Dudoit and J. Fridlyand, “A prediction-based resampling method for estimating the number of clusters in a dataset,” Genome Biol., vol. 3, p. RESEARCH0036, Jun 2002.
• [3] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press, 1996, pp. 226-231.
• [4] T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, and C. D. Bloomfield, “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, pp. 531-537, 1999.
• [5] J. A. Hartigan, “Statistical theory in clustering,” J. Classification, vol. 2, no. 1, pp. 63-76, 1985. [Online]. Available: http://dx.doi.org/10.1007/BF01908064
• [6] L. J. Heyer, S. Kruglyak, and S. Yooseph, “Exploring expression data: identification and analysis of coexpressed genes,” Genome Res., vol. 9, pp. 1106-1115, Nov 1999.
• [7] S. C. Johnson, “Hierarchical clustering schemes,” Psychometrika, vol. 32, pp. 241-254, Sep 1967.
• [8] L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics). Wiley-Interscience, March 2005. [Online]. Available: http://www.worldcat.org/isbn/0471735787
• [9] T. Kohonen, Self-organization and associative memory, ser. Springer Series in Information Sciences. Berlin: Springer-Verlag, 1984, vol. 8.
• [10] W. J. Krzanowski and Y. T. Lai, “A criterion for determining the number of groups in a data set using sum-of-squares clustering,” Biometrics, vol. 44, no. 1, pp. 23-34, 1988. [Online]. Available: http://dx.doi.org/10.2307/2531893
• [11] R. J. Lipshutz, S. P. Fodor, T. R. Gingeras, and D. J. Lockhart, “High density synthetic oligonucleotide arrays,” Nat. Genet., vol. 21, pp. 20-24, Jan 1999.
• [12] J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proc. Fifth Berkeley Sympos. Math. Statist. and Probability (Berkeley, Calif., 1965/66). Berkeley, Calif.: Univ. California Press, 1967, pp. Vol. I: Statistics, pp. 281-297.
• [13] E. E. Schadt, C. Li, C. Su, and W. H. Wong, “Analyzing high-density oligonucleotide gene expression array data,” J. Cell. Biochem., vol. 80, pp. 192–202, Oct 2000.
• [14] R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” Journal Of The Royal Statistical Society Series B, vol. 63, no. 2, pp. 411-423, 2001. [Online]. Available: http://ideas.repec.org/a/bla/jorssb/v63y2001i2p411-423.html