Wavelet-based functional clustering for patterns of high-dimensional dynamic gene expression

Journal of Computational Biology

Volumn 17, Issue 8, 2010, Pages 1067-1080

  Kim, Bong Rae ^a   McMurry, Timothy ^b   Zhao, Wei ^c   Wu, Rongling ^d   Berg, Arthur ^d  

^a Seoul National University   (South Korea)

^b DEPAUL UNIVERSITY   (United States)

^c ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

^d PENN STATE COLLEGE OF MEDICINE   (United States)

Author keywords

Algorithms; statistics

Indexed keywords

ARTICLE; CLUSTER ANALYSIS; DNA MICROARRAY; GENE EXPRESSION PROFILING; METHODOLOGY; TIME;

CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; TIME FACTORS;

EID: 77956083094     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2009.0270     Document Type: Article

References (37)

1. Aboufadel, E., and Schlicker, S. 1999. Discovering wavelets. Wiley.
2. Bar-Joseph, Z., Gerber, G.K., Gifford, D.K., et al. 2003. Continuous representations of time-series gene expression data. J. Comput. Biol. 10, 341-356.
3. De Lichtenberg, U., Jensen, L.J., Fausboll, A., et al. 2005. Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics 21, 1164.
4. Diggle, P., Heagerty, P., Liang, K.Y., et al. 2002. Analysis of Longitudinal Data. Oxford University Press, New York.
5. Donoho, D.L. 1995. De-noising by soft-thresholding. IEEE Trans. Inform. Theory 41, 613-627.
6. Donoho, D.L., and Johnstone, J.M. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425.
7. Eisen, M.B., Spellman, P.T., Brown, P.O., et al. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863.
8. Ernst, J., Nau, G.J., and Bar-Joseph, Z. 2005. Clustering short time series gene expression data. Bioinformatics 21, 159.
9. Ghael, S.P., Sayeed, A.M., and Baraniuk, R.G. 1997. Improved wavelet denoising via empirical wiener filtering. Proc. SPIE 3169, 389-399.
10. Ghosh, D., and Chinnaiyan, A.M. 2002. Mixture modelling of gene expression data from microarray experiments. Bioinformatics 18, 275.
11. Holter, N.S., Maritan, A., Cieplak, M., et al. 2001. Dynamic modeling of gene expression data. Proc. Natl. Acad. Sci. USA 98, 1693.
12. Inoue, L.Y.T., Neira, M., Nelson, C., et al. 2007. Cluster-based network model for time-course gene expression data. Biostatistics 8, 507-525.
13. Jansen, M. 2003. Wavelet thresholding on non-equispaced data. Nonlinear Estimation Classification 261.
14. Jensen, A., and la Cour-Harbo, A. 2001. Ripples in Mathematics: The Discrete Wavelet Transform. Springer Verlag, New York.
15. Johnstone, I.M., and Silverman, B.W. 1997. Wavelet threshold estimators for data with correlated noise. J. R. Statist. Soc. Ser. B 59, 319-351.
16. Kim, B.R., Zhang, L., Berg, A., et al. 2008. A computational approach to the functional clustering of periodic gene expression profiles. Genetics 180, 821-834.
17. Lasser, R. 1996. Introduction to Fourier Series. CRC, Boca Raton, FL.
18. Luan, Y., and Li, H. 2003. Clustering of time-course gene expression data using a mixed-effects model with b-splines. Bioinformatics 19, 474.
19. Ma, P., Castillo-Davis, C.I., Zhong, W., et al. 2006. A data-driven clustering method for time course gene expression data. Nucleic Acids Res. 34, 1261.
20. Mallat, S.G., et al. 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674-693.
21. McLachlan, G.J., Bean, R.W., and Peel, D. 2002. A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18, 413.
22. Ng, S.K., McLachlan, G.J., Wang, K., et al. 2006. A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22, 1745.
23. Ning, L., McMurry, T., Berg, A., et al. 2010. Functional clustering of periodic transcriptional profiles through arma( p,q). PLoS ONE (in press).
24. Park, T., Yi, S.G., Lee, S., et al. 2003. Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 19, 694.
25. Pensky, M., and Vidakovic, B. 2001. On non-equally spaced wavelet regression. Ann. Instit. Statist. Math. 53, 681-690.
26. Qian, J., Stenger, B., Wilson, C.A., et al. 2001. Partslist: a web-based system for dynamically ranking protein folds based on disparate attributes, including whole-genome expression and interaction information. Nucleic Acids Res. 29, 1750.
27. Ramoni, M.F., Sebastiani, P., and Kohane, I.S. 2002. Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA 99, 9121.
28. Rustici, G., Mata, J., Kivinen, K., et al. 2004. Periodic gene expression program of the fission yeast cell cycle. Nat. Genet. 36, 809-817.
29. Spellman, P.T., Sherlock, G., Zhang, M.Q., et al. 1998. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. cell 9, 3273.
30. Storey, J.D., Xiao, W., Leek, J.T., et al. 2005. Significance analysis of time course microarray experiments. Proc. Natl. Acad. Sci. USA 102, 12837.
31. Urbanek, S. 2009. multicore: Parallel processing of R code on machines with multiple cores or CPUs. R package version 0.1-3. Available at: www.rforge.net/multicore/. Accessed June 1, 2010.
32. Vanraes, E., Jansen, M., and Bultheel, A. 2002. Stabilised wavelet transforms for non-equispaced data smoothing. Signal Process. 82, 1979-1990.
33. Vidakovic, B. 1999. Statistical Modeling by Wavelets. Wiley, New York.
34. Walker, J.S. 1999. A Primer on Wavelets and Their Scientific Applications. CRC Press, Boca Raton, FL.
35. Wang, L., Chen, X., Wolfinger, R.D., et al. 2009. A unified mixed effects model for gene set analysis of time course microarray experiments. Statist. Appl. Genet. Mol. Biol. 8, 47.
36. Zapala, M.A., and Schork, N.J. 2006. Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proc. Natl. Acad. Sci. USA 103, 19430.
37. Zhao, L.P., Prentice, R., and Breeden, L. 2001. Statistical modeling of large microarray data sets to identify stimulusresponse profiles. Proc. Natl. Acad. Sci. USA 98, 5631.