From patterns to pathways: Gene expression data analysis comes of age

Nature Genetics

Volumn 32, Issue 4S, 2002, Pages 502-508

  Slonim, Donna K ^a  

^a PFIZER INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; DATA ANALYSIS; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETIC TRANSCRIPTION; PREDICTION; PRIORITY JOURNAL; REVIEW;

CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; FORECASTING; GENE EXPRESSION PROFILING; HUMANS; MODELS, BIOLOGICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; STATISTICS; TRANSCRIPTION, GENETIC;

EUKARYOTA;

EID: 0036899286     PISSN: 10614036     EISSN: 15461718     Source Type: Journal    
DOI: 10.1038/ng1033     Document Type: Review

References (98)

1. Chu, S. et al. The transcriptional program of sporulation in budding yeast. Science 282, 699-705 (1998).
2. DeRisi, J.L., Iyer, V.R. and Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680-686 (1997).
3. Schena, M. et al. Parallel human genome analysis: Microarray-based expression monitoring of 1000 genes. Proc. Natl Acad. Sci. USA 93, 10614-10619 (1996).
4. Wodicka, L., Dong, H., Mittmann, M., Ho, M.H. and Lockhart, D.J. Genome-wide expression monitoring in Saccharomyces cerevisiae. Nat. Biotechnol. 15, 1359-1367 (1997).
5. Churchill, G.A. Fundamentals of experimental design for cDNA microarrays. Nature Genet. 32, 490-495 (2002).
6. Yang, Y.H. and Speed, T. Design issues for cDNA microarray experiments. Nature Rev.Genet. 3, 579-588 (2002).
7. Fambrough, D., McClure, K., Kazlauskas, A. and Lander, E.S. Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes. Cell 97, 727-741 (1999).
8. Holstege, F.C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717-728 (1998).
9. Li, C. and Hung Wong, W. Model-based analysis of oligonucleotide arrays: Model validation, design issues and standard error application. Genome Biol. 2, research0032 (2001).
10. Roberts, C.J. et al. Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. Science 287, 873-880 (2000).
11. Ideker, T., Thorsson, V., Siegel, A.F. and Hood, L.E. Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. J. Comput. Biol. 7, 805-817 (2000).
12. Zar, J.H. Biostatistical Analysis, 663 (Prentice-Hall, Upper Saddle River, NJ, 1999).
13. Tusher, V.G., Tibshirani, R. and Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116-5121 (2001).
14. Golub, T.R. et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 531-537 (1999).
15. Model, F., Adorjan, P., Olek, A. and Piepenbrock, C. Feature selection for DNA methylation based cancer classification. Bioinformatics 17 Suppl 1, S157-S164 (2001).
16. Zhan, F. et al. Global gene expression profiling of multiple myeloma, monoclonal gammopathy of undetermined significance, and normal bone marrow plasma cells. Blood 99, 1745-1757 (2002).
17. Ben-Dor, A., Friedman, N. and Yakhini, Z. Scoring genes for relevance. Technical Report 2000-38 (Institute of Computer Science, Hebrew University, Jerusalem, 2000).
18. Park, P.J., Pagano, M. and Bonetti, M. A nonparametric scoring algorithm for identifying informative genes from microarray data. Pac. Symp. Biocomput. 52-63 (2001).
19. Quackenbush, J. Microarray data normalization and transformation. Nature Genet. 32, 496-501 (2002).
20. Dudoit, S., Yang, Y.-H., Callow, M.J. and Speed, T.P. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Technical Report 578 (Department of Statistics, University of California at Berkeley, Berkeley, CA, 2000).
21. Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65-70 (1979).
22. Westfall, P.H. and Young, S.S. Resampling-Based Multiple Testing, 340 (John Wiley and Sons, New York, 1993).
23. Benjamini, Y. and Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B57, 289-300 (1995).
24. Chatfield, C. The Analysis of Time Series: An Introduction (5th ed.), 283 (Chapman and Hall, London, 1996).
25. Shumway, R.H. and Stoffer, D.S. Time Series Analysis and Its Applications, 560 (Springer Verlag, New York, 2000).
26. 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 95, 14863-14868 (1998).
27. Cho, R.J. et al. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2, 65-73 (1998).
28. Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiaeby microarray hybridization. Mol. Biol. Cell 9, 3273-3297 (1998).
29. Aach, J. and Church, G.M. Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495-508 (2001).
30. Filkov, V., Skiena, S. and Zhi, J. Analysis techniques for microarray time-series data. J. Comput. Biol. 9, 317-330 (2002).
31. Raychaudhuri, S., Stuart, J.M. and Altman, R.B. Principal components analysis to summarize microarray experiments: Application to sporulation time series. Pac. Symp. Biocomput. 455-466 (2000).
32. Landgrebe, J., Wurst, W. and Welzl, G. Permutation-validated principal components analysis of microarray data. Genome Biol 3, research0019 (2002).
33. Holter, N.S. et al. Fundamental patterns underlying gene expression profiles: Simplicity from complexity. Proc. Natl Acad. Sci. USA 97, 8409-8414 (2000).
34. Alter, O., Brown, P.O. and Botstein, D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl Acad. Sci. USA 97, 10101-10106 (2000).
35. Bittner, M. et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536-540 (2000).
36. Khan, J. et al. Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res.58, 5009-5013 (1998).
37. Jain, A.K. and Dubes, R.C. Algorithms for Clustering Data(Prentice-Hall, Englewood Cliffs, NJ, 1988).
38. Wen, X. et al. Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl Acad. Sci. USA 95, 334-339 (1998).
39. Alizadeh, A.A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503-511 (2000).
40. Yona, G. Methods for global organization of all known protein sequences. PhD. thesis (Institute of Computer Science, Hebrew University, Jerusalem, Israel, 1999).
41. Kohonen, T. Self-Organizing Maps (Springer, Berlin, 1997).
42. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907-2912 (1999).
43. Ben-Dor, A., Shamir, R. and Yakhini, Z. Clustering gene expression patterns. J. Comput. Biol. 6, 281-297 (1999).
44. De Smet, F. et al. Adaptive quality-based clustering of gene expression profiles. Bioinformatics 18, 735-746 (2002).
45. Heyer, L.J., Kruglyak, S. and Yooseph, S. Exploring expression data: Identification and analysis of coexpressed genes. Genome Res. 9, 1106-1115 (1999).
46. Sharan, R. and Shamir, R. CLICK: A clustering algorithm with applications to gene expression analysis. Proc. Int. Conf. Intell. Syst. Mol. Biol.8, 307-316 (2000).
47. Yeung, K.Y., Fraley, C., Murua, A., Raftery, A.E. and Ruzzo, W.L. Model-based clustering and data transformations for gene expression data. Bioinformatics 17, 977-987 (2001).
48. Fraley, C. and Raftery, A.E. Model-based clustering, discriminant analysis, and density estimation. J. Amer. Stat. Assoc. 97, 611-631 (2002).
49. Hastie, T. et al. ‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns. Genome Biol 1, research0003 (2000).
50. Yeung, K.Y., Haynor, D.R. and Ruzzo, W.L. Validating clustering for gene expression data. Bioinformatics 17, 309-318 (2001).
51. McShane, L.M. et al. Methods of assessing reproducibility of clustering patterns observed in analysis of microarray data. Bioinformatics 18, 1462-1469 (2002).
52. Kerr, M.K. and Churchill, G.A. Bootstrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments. Proc. Natl Acad. Sci. USA 98, 8961-8965 (2001).
53. Gordon, A.D. Classification (Chapman and Hall/CRC, Boca Raton, FL, 1999).
54. Ben-Hur, A., Elisseeff, A. and Guyon, I. A stability based method for discovering structure in clustered data. Pac. Symp. Biocomput. 6-17 (2002).
55. Tibshirani, R., Walther, G. and Hastie, T. Estimating the number of clusters in a dataset via the gap statistic. J. Roy. Statist. Soc. B63, 411-423 (2001).
56. Khan, J. et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Med. 7, 673-679 (2001).
57. Armstrong, S.A. et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genet. 30, 41-47 (2002).
58. Pomeroy, S.L. et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415, 436-442 (2002).
59. Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869-10874 (2001).
60. van ‘t Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-536 (2002).
61. Chung, C.H., Bernard, P.S. and Perou, C.M. Molecular portraits and the family tree of cancer. Nature Genet 32, 533-540 (2002).
62. Dudoit, S., Fridlyand, J. and Speed, T.P. Comparison of discrimination methods for the classification of tumors using gene expression data. Technical Report 576. (Department of Statistics, University of California at Berkeley, Berkeley, CA, 2000).
63. Tibshirani, R., Hastie, T., Narasimhan, B. and Chu, G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc. Natl Acad. Sci. USA 99, 6567-6572 (2002).
64. Hedenfalk, I. et al. Gene-expression profiles in hereditary breast cancer. N. Engl. J. Med. 344, 539-548 (2001).
65. Ramaswamy, S. et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl Acad. Sci. USA 98, 15149-15154 (2001).
66. Mitchell, T.M. Machine Learning, 414 (WCB McGraw-Hill, Boston, 1997).
67. Califano, A., Stolovitzky, G. and Tu, Y. Analysis of gene expression microarrays for phenotype classification. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 75-85 (2000).
68. Brown, M.P. et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl Acad. Sci. USA 97, 262-267 (2000).
69. Furey, T.S. et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906-914 (2000).
70. Breiman, L. Bagging predictors. Machine Learning 24, 123-140 (1996).
71. Schapire, R.E., Freund, Y., Bartlett, P. and Lee, W.S. Boosting the margin: A new explanation for the effectiveness of voting methods. Annls Stat. 26, 1651-1686 (1998).
72. Schapire, R.E. The strength of weak learnability. Machine Learning 5, 197-227 (1990).
73. Breiman, L. Manual on Setting Up, Using, and Understanding Random Forests v3.1. (University of California at Berkeley, Berkeley, CA, 2002).
74. Shipp, M.A. et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Med. 8, 68-74 (2002).
75. Ben-Dor, A. et al. Tissue classification with gene expression profiles. J. Comput. Biol. 7, 559-583 (2000).
76. Su, A.I. et al. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 61, 7388-7393 (2001).
77. Bo, T. and Jonassen, I. New feature subset selection procedures for classification of expression profiles. Genome Biol. 3, research0017 (2002).
78. Butte, A.J. and Kohane, I.S. Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 418-429 (2000).
79. Liang, S., Fuhrman, S. and Somogyi, R. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp. Biocomput. 18-29 (1998).
80. Friedman, N., Linial, M., Nachman, I. and Pe’er, D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601-620 (2000).
81. Ideker, T.E., Thorsson, V. and Karp, R.M. Discovery of regulatory interactions through perturbation: Inference and experimental design. Pac. Symp. Biocomput. 305-316 (2000).
82. Hartemink, A.J., Gifford, D.K., Jaakkola, T.S. and Young, R.A. Combining location and expression data for principled discovery of genetic regulatory network models. Pac. Symp. Biocomput. 437-449 (2002).
83. Pe’er, D., Regev, A., Elidan, G. and Friedman, N. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl 1, S215-S224 (2001).
84. Segal, E., Taskar, B., Gasch, A., Friedman, N. and Koller, D. Rich probabilistic models for gene expression. Bioinformatics 17 Suppl 1, S243-S252 (2001).
85. Yoo, C., Thorsson, V. and Cooper, G.F. Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data. Pac. Symp. Biocomput. 498-509 (2002).
86. Hartemink, A.J., Gifford, D.K., Jaakkola, T.S. and Young, R.A. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput. 422-433 (2001).
87. Potter, J.D. At the interfaces of epidemiology, genetics and genomics. Nature Rev. Genet. 2, 142-147 (2001).
88. Kohane, I.S. Bioinformatics and clinical informatics: The imperative to collaborate. J. Am. Med. Inform. Assoc.7, 512-516 (2000).
89. Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R. and Kohane, I.S. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl Acad. Sci. USA 97, 12182-12186 (2000).
90. Ideker, T., Ozier, O., Schwikowski, B. and Siegel, A.F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1, S233-S240 (2002).
91. Chiang, D.Y., Brown, P.O. and Eisen, M.B. Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles. Bioinformatics 17 Suppl 1, S49-S55 (2001).
92. Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J. and Church, G.M. Systematic determination of genetic network architecture. Nature Genet.22, 281-285 (1999).
93. Holmes, I. and Bruno, W.J. Finding regulatory elements using joint likelihoods for sequence and expression profile data. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 202-210 (2000).
94. Shatkay, H., Edwards, S., Wilbur, W.J. and Boguski, M. Genes, themes and microarrays: Using information retrieval for large-scale gene analysis. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 317-328 (2000).
95. Masys, D.R. et al. Use of keyword hierarchies to interpret gene expression patterns. Bioinformatics 17, 319-326 (2001).
96. Jenssen, T.K., Laegreid, A., Komorowski, J. and Hovig, E. A literature network of human genes for high-throughput analysis of gene expression. Nature Genet. 28, 21-28 (2001).
97. Staunton, J.E. et al. Chemosensitivity prediction by transcriptional profiling. Proc. Natl Acad. Sci. USA98, 10787-10792 (2001).
98. Radmacher, M.D., McShane, L.M. and Simon, R. A paradigm for class prediction using gene expression profiles. J. Comput. Biol. 9, 505-511 (2002).