Interrelated two-way clustering and its application on gene expression data

International Journal on Artificial Intelligence Tools

Volumn 14, Issue 4, 2005, Pages 577-597

  Tang, Chun ^a   Zhang, Aidong ^a  

^a the State University of New York   (United States)

Author keywords

Clustering; Microarray; Sample analysis

Indexed keywords

EID: 33746208817     PISSN: 02182130     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218213005002272     Document Type: Article

References (64)

1. Alizadeh, Ash A., Eisen, Michael B., Davis, R. Eric, Ma, Chi, Lossos, Izidore S., Rosenwald, Adreas, Boldrick, Jennifer C., Sahet, Hajeer, Tran, Truc, Yu, Xin, Powell, John I., Yang, Liming, Marti, Gerald E., Moore, Tory, Hudson, James Jr, Lu, Lisheng, Lewis, David B., Tibshirani, Robert, Sherlock, Gavin, Chan, Wing C., Greiner, Timothy C., Weisenburger, Dennis D., Armitage, James O., Warnke, Roger, Levy, Ronald, Wilson, Wyndham, Grever, Michael R., Byrd, John C., Botstein, D., Brown, P. O., and Staudt, Louis M. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, Vol.403:503-511, February 2000.
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. Alter O., Brown P.O. and Bostein D. Singular value decomposition for genomewide expression data processing and modeling. Proc. Natl. Acad. Sci. USA, Vol. 97(18): 10101-10106, Auguest 2000.
4. Azuaje, Francisco. Making Genome Expression Data Meaningful: Prediction and Discovery of Classes of Cancer Through a Connectionist Learning Approach. In Proceedings of IEEE International Symposium on Bioinformatics and Biomedical Engineering, pages 208-213, 2000.
5. 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.
6. Ben-Dor A., Friedman N. and Yakhini Z. Class discovery in gene expression data. In Proc, Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB 2001), pages 31-38. ACM Press, 2001.
7. Ben-Dor A., Shamir R. and Yakhini Z. Clustering gene expression patterns. Journal of Computational Biology, 6(3/4):281-297, 1999.
8. Bhadra D. and Garg A. An interactive visual framework for detecting clusters of a multidimensional dataset. Technical Report 2001-03, Dept. of Computer Science and Engineering, University at Buffalo, NY., 2001.
9. Biedl T., Brejova B., Demaine E.D., Hamel A.M. and Vinar T. Optimal Arrangement of Leaves in the Tree Representing Hierarchical Clustering of Gene Expression Data. Technical report 2001-14, University of Waterloo, Canada, 2001.
10. Brazma, Alvis and Vilo, Jaak. Minireview: Gene expression data analysis. Federation of European Biochemical societies, 480:17-24, June 2000.
11. 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.
12. Chen J.J., Wu R., Yang P.C., Huang J.Y., Sher Y.P., Han M.H., Kao W.C., Lee P.J., Chiu T.F., Chang F., Chu Y.W., Wu C.W. and Peck K. Profiling expression patterns and isolating differentially expressed genes by cDNA microarray system with colorimetry detection. Genomics, 51:313-324, 1998.
13. DeRisi J., Penland L., Brown P.O., Bittner M.L., Meltzer P.S., Ray M., Chen Y., Su Y.A. and Trent J.M. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nature Genetics, 14:457-460, 1996.
14. Devore, Jay L. Probability and Statistics for Engineering and Sciences. Brook/Cole Publishing Company, 1991.
15. 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.
16. Ermolaeva O., Rastogi M., Pruitt K.D., Schuler G.D., Bittner M.L., Chen Y., Simon R., Meltzer P., Trent J.M. and Boguski M.S. Data management and analysis for gene expression arrays. Nature Genetics, 20:19-23, 1998.
17. Furey T.S., Cristianini N., Duffy N., Bednarski D.W., Schummer M., and Haussler D. Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics, Vol.16(10): 909-914, 2000.
18. Getz G., Levine E. and Domany E. Coupled two-way clustering analysis of gene microarray data. Proc. Natl. Acad. Sci. USA, Vol. 97(22):12079-12084, October 2000.
19. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gassenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield D.D. and Lander E.S. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, Vol. 286(15):531-537, October 1999.
20. Hakak Y., Walker J.R., Li C., Wong W.H., Davis K.L., Buxbaum J.D., Haroutunian V. and Fienberg A.A. Genome-Wide Expression Analysis Reveals Dysregulation of Myelination-Related Genes in Chronic Schizophrenia. Proc. Natl. Acad. Sci. USA, Vol. 98(8):4746-4751, April 2001.
21. Han, Jiawei and Kamber, Micheline. Data Mining: Concept and Techniques. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers, August 2000.
22. Hartigan, J.A. Clustering Algorithm. John Wiley and Sons, New York., 1975.
23. Hartigan, J.A. and Wong, M.A. Algorithm AS136: a k-means clustering algorithms. Applied Statistics, 28:100-108, 1979.
24. Hastie T., Tibshirani R., Boststein D. and Brown P. Supervised harvesting of expression trees. Genome Biology, Vol. 2(1):0003.1-0003.12, January 2001.
25. Heller R.A., Schena M., Chai A., Shalon D., Bedilion T., Gilmore J., Woolley D.E. and Davis R.W. Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. Proc. Natl. Acad. Sci. USA, 94:2150-2155, 1997.
26. Herrero J., Valencia A. and Dopazo J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics, 17:126-136, 2001.
27. Holter NS., Mitra M., Maritan A., Cieplak M., Banavar JR., Fedoroff NV. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci, 97(15):8409-8414, 2000.
28. Iyer V.R., Eisen M.B., Ross D.T., Schuler G., Moore T., Lee J.C.F., Trent J.M., Staudt L.M., Hudson Jr. J., Boguski M.S., Lashkari D., Shalon D., Botstein D. and Brown P.O. The transcriptional program in the response of human fibroblasts to serum. Science, 283:83-87, 1999.
29. Jiang M., Ryu J., Kiraly M., Duke K., Reinke V. and Kim S.K. Genome-Wide Analysis of Developmental and Sex-Regulated Gene Expression Profiles in Caenorhabditis Elegans. Proc. Natl. Acad. Sci. USA, Vol. 98(1):218-223, January 2001.
30. Jiang S., Tang C., Zhang L., Zhang A. and Ramanathan M. A maximum entropy approach to classifying gene array data sets. In Proc. of Workshop on Data mining for genomics, First SIAM International Conference on Data Mining, 2001.
31. Jorgensen, Anna. Clustering excipient near infrared spectra using different chemometric methods. Technical report, Dept. of Pharmacy, University of Helsinki, 2000.
32. Kohonen T. Self-Organization and Associative Memory. Spring-Verlag, Berlin, 1984.
33. Li, Wentian. Zipf's Law in Importance of Genes for Cancer Classification Using Microarray Data. Lab of Statistical Genetics, Rockefeller University, April 2001.
34. Luscombe N. M., Greenbaum D. and Gerstein M. Review: What is bioinformatics? An introduction and overview. Yearbook of Medical Informatics, pages 83-99, 2001.
35. Manduchi E., Grant G.R., McKenzie S.E., Overton G.R., Surrey S. and Stoeckert C.J.Jr. Generation of patterns from gene expression data by assigning confidence to differentially expressed genes. Bioinformatics, Vol. 16(8):685-698, 2000.
36. Martin K.J., Graner E., Li Y., Price L.M., Kritzman B.M., Fournier M.V., Rhei E. and Pardee A.B. High-Sensitivity Array Analysis of Gene Expression for the Early Detection of Disseminated Breast Tumor Cells in Peripheral Blood. Proc. Natl. Acad. Sci. USA, Vol. 98(5):2646-2651, February 2001.
37. Mody M., Cao Y., Gui Z., Tay K.Y., Shyong A., Shimizu E., Pham K., Schultz P., Welsh D. and Tsien J.Z. Genome-Wide Gene Expression Profiles of the Developing Mouse Hippocampus. Proc. Natl. Acad. Sci. USA, Vol. 98(15):8862-8867, July 2001.
38. Moler E.J., Chow M.L. and Mian I.S. Analysis of Molecular Profile Data Using Generative and Discriminative Methods. Physiological Genomics, Vol. 4(2): 109-126, 2000.
39. Nguyen LT., Ramanathan M., Munschauer F., Brownscheidle O., Krantz S., Umhauer M., et al. Flow cytometric analysis of in vitro proinflammatory cytokine secretion in peripheral blood from multiple sclerosis patients. J Clin Immunol, 19(3):179-185, 1999.
40. Park P.J., Pagano M., and Bonetti M. A Nonparametric Scoring Algorithm for Identifying Informative Genes from Microarray Data. In Pacific Symposium on Biocomputing, pages 52-63, 2001.
41. Pavlidis P., Weston J., Cai J. and Grundy W.N. Gene Functional Classification from Heterogeneous Data. In RECOMB 2001: Proceedings of the Fifth Annual International Conference on Computational Biology, pages 249-255. ACM Press, 2001.
42. Perou C.M., Jeffrey S.S., Rijn, M.V.D., Rees C.A., Eisen M.B., Ross D.T., Pergamenschikov A., Williams C.F., Zhu S.X., Lee, J.C.F., Lashkari D., Shalon D., Brown P.O., and Bostein D. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc. Natl. Acad. Sci. USA, Vol. 96(16): 9212-9217, August 1999.
43. Rand, W.M. Objective criteria for evaluation of clustering methods. Journal of the American Statistical Association, 1971.
44. Schena M., Shalon D., Davis R.W. and Brown P.O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 270:467-470, 1995.
45. Schena M., Shalon D., Heller R., Chai A., Brown P.O., and Davis R.W. Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes. Proc. Natl. Acad. Sci. USA, Vol. 93(20): 10614-10619, October 1996.
46. Shalon D., Smith S.J. and Brown P.O. A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. Genome Research, 6:639-645, 1996.
47. Slonim O.K., Tamayo P., Mesirov J.P., Golub T.R. and Lander E.S. Class Prediction and Discovery Using Gene Expression Data. In RECOMB 2000: Proceedings of the Fifth Annual International Conference on Computational Biology. ACM Press, 2000.
48. Spellman PT., Sherlock G., Zhang MQ., Iyer VR., Anders K., Eisen MB., et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell, 9(12):3273-3297, 1998.
49. Tamayo P., Solni D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E.S. and Golub T.R,. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA, Vol. 96(6):2907-2912, March 1999.
50. Tang C., Zhang L., Zhang A. and Ramanathan M. Interrelated two-way clustering: An unsupervised approach for gene expression data analysis. In Proceeding of BIBE2001: 2nd IEEE International Symposium on Bioinformatics and Bioengineering, pages 4148, Bethesda, Maryland, November 4-5 2001.
51. Tavazoie, S., Hughes, D., Campbell, M.J., Cho, R.J. and Church, G.M. Systematic determination of genetic network architecture. Nature Genet, pages 281-285, 1999.
52. 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.
53. Tusher V.G., Tibshirani R. and Chu G. Significance Analysis of Microarrays Applied to the Ionizing Radiation Response. Proc. Natl. Acad. Sci. USA,Vol 98(9):5116-5121, April 2001.
54. Vingron, M. and Hoheisel, J. Computational Aspects of Expression Data. J. Mol. Med., 77:3-7, 1999.
55. Virtaneva K., Wright F.A., Tanner S.M., Yuan B., Lemon W.J., Caligiuri M.A., Bloomfield C.D., Chapelle A. de la and Krahe R. Expression Profiling Reveals Fundamental Biological Differences in Acute Myeloid Leukemia with Isolated Trisomy 8 and Normal Cytogenetic. Proc. Nail. Acad. Sci. USA, Vol. 98(3):1124-1129, January 2001.
56. Welford S.M., Gregg J., Chen E., Garrison D., Sorensen P.H., Denny C.T. and Nelson S.F. Detection of differentially expressed genes in primary tumor tissues using representational differences analysis coupled to microarray hybridization. Nucleic Acids Research, 26:3059-3065, 1998.
57. Welsh J.B., Zarrinkar P.P., Sapinoso L.M., Kern S.G., Behling C.A., Monk B.J., Lockhart D.J., Burger R.A. and Hampton G.M. Analysis of Gene Expression Profiles in Normal and Neoplastic Ovarian Tissue Samples Identifies Candidate Molecular Markers of Epithelial Ovarian Cancer. Proc. Natl Acad. Sci. USA, Vol. 98(3): 1176-1181, January 2001.
58. 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.
59. 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.
60. Yeung K.Y., Haynor D.R. and Ruzzo W.L. Validating Clustering for Gene Expression Data. Bioinforrnatics, Vol.17(4):309-318, 2001.
61. Yong V., Chabot S., Stuve Q. and Williams G. Interferon beta in the treatment of multiple sclerosis: mechanisms of action. Neurology, 51:682-689, 1998.
62. Zhang H., Yu C.Y., Singer B., and Xiong M. Recursive Partitioning for Tumor Classification with Gene Expression Microarray Data. Proc. Natl. Acad. Sci. USA, Vol. 98(12):6730-6735, June 2001.
63. Zhang L., Tang C., Shi Y., Song Y., Zhang A. and Ramanathan M. VizCluster: An Interactive Visualization Approach to Cluster Analysis and Its Application on Microarray Data. In Second SIAM International Conference on Data Mining (SDM'2002), pages 19-40, Arlington, VA, April 11-13 2002.
64. Zou S., Meadows S., Sharp L., Jan L.Y. and Jan Y. N. Genome-Wide Study of Aging and Oxidative Stress Response in Drosophila Melanogaster. Proc. Natl. Acad. Sci. USA, Vol. 97(25):13726-13731, December 2000.