How to cluster gene expression dynamics in response to environmental signals

Briefings in Bioinformatics

Volumn 13, Issue 2, 2012, Pages 162-174

  Wang, Yaqun ^a   Xu, Meng ^b   Wang, Zhong ^a   Tao, Ming ^c   Zhu, Junjia ^d   Wang, Li ^d   Li, Runze ^a   Berceli, Scott A ^e   Wu, Rongling ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b NANJING FORESTRY UNIVERSITY   (China)

^c HARVARD MEDICAL SCHOOL   (United States)

^d PENN STATE COLLEGE OF MEDICINE   (United States)

^e UNIVERSITY OF FLORIDA   (United States)

Author keywords

Dynamic gene expression; Functional clustering; Gene environment interaction; Mixture model

Indexed keywords

ALGORITHM; ARTICLE; CELL DIFFERENTIATION; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENETIC DATABASE; GENOTYPE ENVIRONMENT INTERACTION; METHODOLOGY;

ALGORITHMS; CELL DIFFERENTIATION; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATABASES, GENETIC; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE-ENVIRONMENT INTERACTION;

EID: 84863283413     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbr032     Document Type: Article

References (65)

1. Arbeitman MN, Furlong EM, Imam F, et al. Gene expression during the life cycle of Drosophila melanogaster. Science 2002;297:2270-5.
2. Rustici G, Mata J, Kivinen K, et al. Periodic gene expression program of the fission yeast cell cycle. Nat Genet 2004;36: 809-17.
3. Cohen AA, Geva-Zatorsky N, Eden E, et al. Dynamic proteomics of individual cancer cells in response to a drug. Science 2008;322:1511-6.
4. Holter NS, Maritan A, Cieplak M. Dynamic modeling of gene expression data. Proc Natl Acad Sci USA 2001;98: 1693-8.
5. Zhao LP, Prentice R, Breeden L. Statistical modeling of large microarray data sets to identify stimulus-response profiles. Proc Natl Acad Sci USA 2001;98:5631-6.
6. Ramoni MF, Sebastiani P, Kohane IS. Cluster analysis of gene expression dynamics. Proc Natl Acad Sci USA 2002;99: 9121-6.
7. Park T, Yi SG, Lee S, et al. Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 2003;19:694-703.
8. Bar-Joseph Z, Gerber GK, Gifford DK, et al. Continuous representations of time-series gene expression data. JComput Biol 2003;10:341-56.
9. Luan Y, Li H. Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 2003;19:474-82.
10. Ernst J, Nau GJ, Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics 2005;21:i159-68.
11. Ma P, Castillo-Davis CI, Zhong W, et al. A data-driven clustering method for time course gene expression data. Nucleic Acids Res 2006;34:1261-9.
12. Inoue LY, Neira M, Nelson C, et al. Cluster-based network model for time-course gene expression data. Biostatistics 2007;8:507-25.
13. Muller HG, Chiou JM, Leng X. Inferring gene expression dynamics via functional regression analysis. BMC Bioinformatics 2008;9:60.
14. Kim B-R, Zhang L, Berg A, et al. A computational approach to the functional clustering of periodic gene expression profiles. Genetics 2008;180:821-34.
15. Kim B-R, McMurry T, Zhao W, et al. Wavelet-based functional clustering for high-dimensional dynamic gene expression patterns. J Comput Biol 2010;17:1067-80.
16. Smith EN, Kruglyak L. Gene-environment interaction in yeast gene expression. PLoS Biol 2008;6:e83.
17. McAdams HH, Srinivasan B, Arkin AP. The evolution of genetic regulatory systems in bacteria. Nat Rev Genet 2004;5: 169-78.
18. Seshasayee A, Bertone P, Fraser G, et al. Transcriptional regulatory networks in bacteria: from input signals to output responses. Curr Opin Microbiol 2006;9:511-9.
19. Wittkopp P. Variable gene expression in eukaryotes: a network perspective. J Exp Biol 2007;210:1567-75.
20. Zhao W, Zhu J, Gallo-Meagher M, et al. A unified statistical model for functional mapping of genotype environment interactions for ontogenetic development. Genetics 2004; 168:1751-62.
21. Zhao W, Ma C-X, Cheverud JM, etal. A unifying statistical model for QTL mapping of genotype-sex interaction for developmental trajectories. Physiol Genom 2004;19: 218-27.
22. Landry CR, Oh J, Hartl DL, et al. Genome-wide scan reveals that genetic variation for transcriptional plasticity in yeast is biased towards multi-copy and dispensable genes. Gene 2006;366:343-51.
23. Li Y, Alvarez OA, Gutteling EW, et al. Mapping determinants of gene expression plasticity by genetical genomics in C elegans. PLoS Genet 2006;2:e222.
24. DeLichtenberg U, Jensen LJ, Brunak S, et al. Dynamic complex formation during the yeast cell cycle. Science 2005;307:724-7.
25. Jiang Z, Wu L, Miller BL, et al. A novel vein graft model: adaptation to differential flow environments. Am J Physiol Heart Circ Physiol 2004;286:H240-5.
26. Fernandez CM, Goldman DR, Jiang Z, et al. Impact of shear stress on early vein graft remodeling: a biomechanical analysis. Ann Biomed Eng 2004;32:1484-93.
27. Spellman PT, Sherlock G, Zhang MQ, et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998;9:3273-97.
28. Whitfield ML, Sherlock G, Saldanha AJ, et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 2002;13: 1977-2000.
29. Shedden K, Cooper S. Analysis of cell-cycle gene expression in Saccharomyces cerevisiae using microarrays and multiple synchronization methods. Nucleic Acids Res 2002;30:2920-9.
30. Breeden LL. Periodic transcription: a cycle within a cycle. Curr Biol 2003;13:R31-8.
31. Rustici G, Mata J, Kivinen K, et al. Periodic gene expression program of the fission yeast cell cycle. Nat Genet 2004;36: 809-17.
32. Ahdesmaki M, Lhdesmaki H, Pearson R, et al. Robust detection of periodic time series measured from biological systems. BMC Bioinformatics 2005;6:117.
33. von Bertalanffy L. Quantitative laws for metabolism and growth. Quart Rev Biol 1957;32:217-31.
34. Richards FJ. A flexible growth function for empirical use. J Exp Bot 1959;10:290-300.
35. West GB, Brown JH, Enquist BJ. A general model for ontogenetic growth. Nature 2001;413:628-31.
36. Guiot C, Degiorgis PG, Delsanto PP, et al. Does tumor growth follow a 'universal law'? Theor Biol 2003;225: 147-51.
37. Guiot C, Delsanto PP, Carpinteri A, et al. The dynamic evolution of the power exponent in a universal growth model of tumors. JTheor Biol 2006;240:459-63.
38. Li N, Das K, Wu RL. Functional mapping of human growth trajectories. JTheor Biol 2010;261:33-42.
39. Perelson AS, Neumann AU, Markowitz M, et al. HIV-1 dynamics in vivo: Virion clearance rate, infected cell life-span, and viral generation time. Science 1996;271: 1582-6.
40. Ahn K, Luo jt, Berg A, et al. Functional mapping of drug response with pharmacodynamic-pharmcokinetic principles. Trend Pharmacol Sci 2010;31:306-11.
41. Kingsolver JG, Woods HA. Thermal sensitivity of growth and feeding in Manduca sexta caterpillars. Physiol Zool 1997; 70:631-8.
42. Tobalske BW, Hedrick TL, Dial KP, et al. Comparative power curves in bird flight. Nature 2003;421:363-6.
43. Lin M, Zhao W, Wu RL. A statistical framework for genetic association studies of power curves in bird flight. Biol Proced Online 2006;8:164-74.
44. Wu JS, Zhu J, Zeng YR, et al. Functional mapping of norm reactions to environmental signals. Genet Res 2007; 89:27-38.
45. Daub CO, Steuer R, Selbig J, et al. Estimating mutual information using B-spline functions-an improved similarity measure for analysing gene expression data. BMC Bioinformatics 2004;5:118.
46. Borgwardt KM, Vishwanathan SV, Kriegel HP. Class prediction from time series gene expression profiles using dynamical systems kernels. Pac Symp Biocomput 2006;11: 547-58.
47. Silverman B. Spline smoothing: the equivalent variable kernel method. Ann Stat 1984;12:898-916.
48. Lin M, Wu RL. A joint model for nonparametric functional mapping of longitudinal trajectories and time-to-events. BMC Bioinformatics 2006;7:138.
49. Cui YH, Zhu J, Wu RL. Functional mapping for genetic control of programmed cell death. Physiol Genom 2006;25: 458-69.
50. Cui YH, Wu RL, Casella G. Nonparametric functional mapping quantitative trait loci underlying programmed cell death. Stat Appl GenetMol Biol 2008;7:Article 4.
51. Yang RQ, Gao HJ, Wang X, et al. A semiparametric model for composite functional mapping of dynamic quantitative traits. Genetics 2007;177:1859-70.
52. Huskova M, Sen OK. On sequentially adaptive asymptotically efficient rank statistics. Sequent Analy 1985;4:125-51.
53. McKay MD. Non-parametric variance-based methods for assessing uncertainty importance. Reliability Engin Syst Safety 1997;57:267-79.
54. Meyer K. Random regressions to model phenotypic variation in monthly weights of Australian beef cows. Livestock Prod Sci 2000;65:19-38.
55. Diggle PJ, Heagerty P, Liang KY, et al. Analysis of LongitudinalData. Oxford: Oxford University Press, 2002.
56. Box G, Jenkins G, Reinsel G. Time Series Analysis:Forecasting and Control. San-Francisco: Holden-day, 1976.
57. Haddad J. On the closed form of the covariance matrix and its inverse of the causal ARMA process. J Time Series Anal 2004;25:443-8.
58. Brockwell P, Davis R. Time Series:Theory and Methods. New York: Springer, 1991.
59. Zimmerman DL, Nunez-Anton V. Parametric modeling of growth curve data: an overview (with discussion). Test 2001; 10:1-73.
60. Zhao W, HouW, Littell RC, et al. Structured antedependence models for functional mapping of multivariate longitudinal quantitative traits. Stat Appl Mol Genet Biol 2005;4:Article 33.
61. Fan J, Yao Q. Nonlinear Time Series: Nonparametric and ParametricMethods. New York: Springer, 2003.
62. Cressie N, Huang H-C. Classes of nonseparable, spatiotemporal stationary covariance functions. J Am Stat Assoc 1999;94:1330-40.
63. Gneiting T. Nonseparable, stationary covariance functions for space-time data. J Am Stat Assoc 2002;97:590-600.
64. Gneiting T, Genton M, Guttorp P. Geostatistical space-time models, stationary, separability and full symmetry. In: Finkenstadt B, Held L, Isham V, (eds). Statistical Methods for Spatio-temporal Systems, (Monographs on Statistics and Applied Probability). Boca Raton, Florida: Chapman and Hall/CRC, 2006.
65. Yap JS, Li Y, Das K, et al. Functional mapping of reaction norms to multiple environmental signals through nonparametric covariance estimation. BMC Plant Biol 2011;11:23.