Overview of standard clustering approaches for gene microarray data analysis

DNA Microarrays and Related Genomics Techniques: Design, Analysis, and Interpretation of Experiments

Volumn , Issue , 2005, Pages 131-158

  Garrett Mayer, Elizabeth ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

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EID: 85057406046     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (53)

1. M.E. Garber, O.G. Troyanskaya, K. Schluens, S. Petersen, Z. Thaesler, M. Pacyna-Gengelbach, M. van de Rijn, G.D. Rosen, C.M. Perou, R.I. Whyte, R.B. Altman, P.O. Brown, D. Botstein, and I. Petersen, Diversity of gene expression in adenocarcinoma of the lung. Proceedings of the National Academy of Sciences, USA, 98: 13784-13789, 2001.
2. A. Rosenwald, G. Wright, A. Wiestner, W.C. Chan, J.M. Connors, E. Campo, R.D. Gascoyne, T.M. Grogan, H.K. Muller-Hermelink, E.B. Smeland, M. Chiorazzi, J.M. Giltnane, E.M. Hurt, H. Zhao, A.L.S. Henrickson, L. Yang, J. Powell, W.H. Wilson, E.S. Jaffe, R. Simon, R.D. Klausner, E. Montserrat, F. Bosch, T.C. Greiner, D.D. Weisenburger, W.G. Sanger, B.J. Dave, J.C. Lynch, J. Vose, J.O. Armitage, R.I. Fisher, T.P. Miller, M. LeBlanc, G. Ott, S. Kvaloy, H. Holte, J. Delabie, L.M. Staudt, and L.L.M.P. Project, The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. New England Journal of Medicine 346: 1937-1947, 2002.
3. J. Quackenbush, Computational analysis of microarray data. Nature 2: 418-427, 2001.
4. R. Tibshirani, T. Hastie, M. Eisen, D. Ross, D. Botstein, and P.O. Brown, Clustering methods for the analysis of DNA microarray data. Department of Statistics, Stanford, CA: Stanford University, 1999.
5. A.D. Gordon, Classification, 2nd ed. New York: Chapman & Hall/CRC, 1999.
6. I.J. Blader, I.D. Manger, and J.C. Boothroyd, Microarray analysis reveals previously unknown changes in toxoplasma gondii-infected human cells. Journal of Biological Chemistry 276: 24223-24231, 2001.
7. 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 yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9: 3273-3297, 1998.
8. X. Chen, S.T. Cheung, S. So, S.T. Fan, C. Barry, J. Higgins, K.M. Lai, J. Ji, S. Dudoit, I.O. Ng, M. Van De Rijn, D. Botstein, and P.O. Brown, Gene expression patterns in human liver cancers. Molecular Biology of the Cell 13: 1929-1939, 2002.
9. X. Wen, S. Fuhrman, G.S. Michaels, D.B. Carr, S. Smith, J.L. Barker, andR. Somogyi, Large-scale temporal gene expression mapping of central nervous system development. Proceedings of the National Academy of Sciences, USA 95: 334-339, 1998.
10. M.B. Eisen, P.T. Spellman, P.O. Brown, and D. Botstein, Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, USA 95: 14863-14868, 1998.
11. B.S. Everitt, S. Landau, and M. Leese, Cluster Analysis, 4th ed. London: Edward Arnold, 2001.
12. L. Kaufman and PJ. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics. New York: John Wiley & Sons, Inc., 1990.
13. R.A. Johnson and D. Wichern, Applied Multivariate Statistical Analysis, 4th ed. Upper Saddle River, NJ: Prentice Hall, 1998.
14. P. MacNaughton-Smith, W. Williams, M.B. Dale, and L.G. Mockett, Dissimilarity analysis: a new technique of hierarchical sub-division. Nature 202: 1034-1035, 1964.
15. J.A. Hartigan and M.A. Wong, A. ¿-means clustering algorithm. Applied Statistics28: 100-108, 1979.
16. T. Kohonen, Analysis of a simple self-organizing process. Biological Cybernetics, 43: 59-69, 1982.
17. T. Kohonen, Self-Organizing Maps. Berlin: Springer-Verlag, 1995.
18. P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E.S. Lander, and T.R. Golub, Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences, USA 96: 2907-2912, 1999.
19. B.D. Ripley, Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press, 1996.
20. B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice Hall, 1992.
21. A. Ben-Dor and Z. Yakhini. Clustering gene expression patterns. In Proceedings of the 3rd Annual International Conference on Computational Molecular Biology (RECOMB99). Lyon, France, 1999.
22. S.K. Kachigan, Multivariate Statistical Analysis, 2nd ed. New York: Radius Press, 1991.
23. B.S. Everitt and G. Dunn, Applied Multivariate Data Analysis, 2nd ed. New York: Oxford University Press, Inc., 2001.
24. J.B. Kruskal, Multidimensional scaling by optimizing goodness of fit to a non-metric hypotheses. Psychometrika 29: 1-27, 1964.
25. K.Y. Yeung, and W.L. Ruzzo, Principal component analysis for clustering gene expression data. Bioinformatics 17: 763-774, 2001.
26. J, Quackenbush, Computational analysis of microarray data. Nature Reviews in Genetics, 2: 418-427, 2001.
27. S. Knudsen, A Biologist’s Guide to Analysis of DNA Microarray Data. New York: John Wiley & Sons, 2002.
28. S. Raychaudhuri, J.M. Stuart, and R.B. Altman. Principal components analysis to summarize microarray experiments: application to sporulation time series. In Proceedings ofthe 5th Pacific Symposium on Biocomputing, Waikiki, Hawaii, 2000.
29. J. Khan, R. Simon, M. Bittner, Y. Chen, S. Leighton, T. Pohida, P.D. Smith, Y. Jiang, G.C. Gooden, J.M. Trent, and P. Meltzer, Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Research 58: 5008-5013, 1998.
30. M. Bittner, P. Meltzer, Y. Chen, Y. Jiang, E. Seftor, M. Hendrix, M Radmacher, R. Simon, Z. Yakhini, A. Ben-Dor, N. Sampas, E. Dougherty, E. Wang, F. Marincola, C. Gooden, J. Lueders, A. Glatfelter, P. Pollock, J. Carpten, E. Gillanders, D. Leja, K. Dietrich, C. Beaudry, M. Berens, D. Alberts, and V. Sondak, Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406: 536-540, 2000.
31. M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H. Zuzan, J.R. Marks, and J.R. Nevins, Predicting the clinical status of human breast cancer using gene expression profiles. Proceedings of the National Academy of Sciences, USA 98: 11462-11467, 2001.
32. T. Hastie, R. Tibshirani, M. Eisen, and P. Brown, Gene Shaving: A New Class of Clustering Methods for Expression Arrays. Stanford, CA: Stanford University, 2000.
33. A. Butte, P. Tamayo, D. Slonim, T.R. Golub, and I.S. Kohane, Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proceedings of the National Academy of Sciences, USA 97: 12182-12186, 2000.
34. J. Herrero, A. Valencia, and J. Dopazo, A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17: 126-136, 2001.
35. E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller, Rich probabilistic models for gene expression. Bioinformatics, 17: S243-S252, 2002.
36. C. Fraley and A.E. Raftery, How many clusters? Which clustering method? Answers via model-based cluster analysis. Computer Journal 41: 578-588, 1998.
37. G. Chen, S.A. Jaradat, and Banerjee, Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data. Statistica Sinica 12: 241-262, 2002.
38. R. Shamir and R. Sharan, Algorithmic Approaches to Clustering Gene Expression Data. Current Topics in Computational Molecular Biology, Boston: MIT Press, 2002.
39. PJ. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computation and Applied Mathematics 20: 53-65, 1987.
40. J. Vilo, A. Brazma, I. Jonassen, A. Robinson, and E. Ukkonenen, Mining for putative regulatory elements in the yeast genome using gene expression data. Intelligent Systems for Molecular Biology 8: 384-394, 2000.
41. R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a dataset via the Gap statistic. Journal of the Royal Statistical Society, Series B 63: 411-423, 2000.
42. K. Zhang and H. Zhao, Assessing reliability of gene clusters from gene expression data. Functional and Integrative Genomics 1: 156-173, 2000.
43. L. Breiman, Using Convex Pseudo-Data to Increase Prediction Accuracy. Berkeley: Statistics Department, U.C. Berkeley, 1998.
44. S. Dudoit, J. Fridlyand, and T.P. Speed, Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97: 77-87, 2002.
45. T. Margush and F.R. McMorris, Consensus n-trees. Bulletin of Mathematical Biology43: 239-244, 1981.
46. G. Schwarz, Estimating the dimension of a model. The Annals of Statistics 6: 461-464, 1978.
47. S. Dudoit and J. Fridlyand, A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology 3: 0036.1-0036.21, 2002.
48. G. Parmigiani, E. Garrett, R. Anbazhagan, and E. Gabrielson, A statistical framework for expression-based molecular classification in cancer. Journal of the Royal Statistical Society, Series B (with discussion) 64: 717-736, 2002.
49. C. Fraley and A.E. Raftery, MCLUST: software for model-based cluster analysis. Journal of Classification 16: 297-306, 1999.
50. J. Vilo, M. Kapushesky, P. Kemmeren, U. Sarkans, and A. Brazma, Expression profiler. In The Analysis ofGene Expression Data: Methods and Software, G. Parmigiani, E. Garrett, R. Irizarry, and S. Zeger (eds.). New York, Springer-Verlag, 2002.
51. A.I. Saeed, V. Sharov, J. White, J. Li, W. Liang, N. Bhagabati, J. Braisted, M. Klapa, T. Currier, M. Thiagarajan, A. Sturn, M. Snuffin, A. Rezantsev, D. Popov, A. Ryltsov, E. Kostukovich, I. Borisovsky, Z. Liu, A. Vinsavich, V. Trush, and J. Quacken-bush, TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34: 374-378, 2003.
52. A. Sturn, J. Quackenbush, andZ. Trajanoski 1, Genesis: cluster analysis of microarray data. Bioinformatics 18: 207-208, 2002.
53. S. Tavajoie, J.D. Hughes, B.J. Campbell, R.J. Cho, and G.M. Church, Systematic determination of genetic network architecture. Nature Genetics 22: 281-285, 1999.