Clustering periodic patterns of gene expression based on fourier approximations

Current Genomics

Volumn 7, Issue 3, 2006, Pages 197-203

  Kim, Bong Rae ^a   Littell, Ramon C ^a   Wu, Rongling ^a,b  

^a University of Florida   (United States)

^b ZHEJIANG A F UNIVERSITY   (China)

Author keywords

Cluster analysis; Fast Fourier transform (FFT); Fourier series approximation; Microarray; Mixture model

Indexed keywords

ALGORITHM; BIOLOGICAL RHYTHM; BIOTECHNOLOGY; CELL ACTIVITY; CELL CYCLE; CLUSTER ANALYSIS; CONTROLLED STUDY; CORRELATION COEFFICIENT; DNA MICROARRAY; FOURIER ANALYSIS; GENE EXPRESSION PROFILING; GENOMICS; MOLECULAR MECHANICS; NONHUMAN; REVIEW;

EID: 33746921310     PISSN: 13892029     EISSN: None     Source Type: Journal    
DOI: 10.2174/138920206777780229     Document Type: Review

References (25)

1. Brown, P. O., Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 1999, 21: 33-37.
2. Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D., Futcher, B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 1998, 9: 3273-3297.
3. Eisen, M. B., Spellman, P. T., Brown, P. O., Botstein, D. Clustering analysis and display of genome-wide expression patterns. Proc. Natl. Acad. SCi. USA 1998, 95: 14863-14868.
4. Ramoni, M. F., Sebastiani, P., Kohane, I. S. Cluster analysis of gene expression dynamics. Proc. Natl. Acad. Sci. USA 2002, 99: 9121-9126.
5. Ghosh, D., Chinnaiyan, A. M. Mixture modeling of gene expression data from microarray experiments. Bioinformatics 2002, 18: 275-286.
6. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., Lander, E. S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999, 286: 531-537.
7. Lossos, I. S., Alizadeh, A. A., Eisen, M. B., Chan, W. C., Brown, P. O., Botstein, D., Staudt, L. M., Levy, R. Ongoing immunoglobulin somatic mutation in germinal center B cell-like but not in activated B cell-like diffuse large cell lymphomas. PNAS 2000, 97: 10209-10213.
8. Holter, N. S., Maritan, A., Cieplak, M., Fedoroff, N. V., Barnavar, J. R. Dynamic modeling of gene expression data. Proc. Natl. Acad. Sci. USA 2001, 98: 1693-1698.
9. Zhao, L. P., Prentice, R., Breeden, L. Statistical modeling of large microarray data sets to identify stimulus-response profiles. PNAS 2001, 98: 5631-5636.
10. Bar-Joseph, Z. Analyzing time series gene expression data. Bioinformatics 2004, 20: 2493-2503.
11. Park, T., Yi, S. G., Lee, S. Statistical tests for identifying differentially expressed gene in time-course microarray experiments. Bioinformatics 2003, 19: 694-703.
12. De Lichtenberg, U., Jensen, L. J., Brunak, S., Bork, P. Dynamic complex formation during the yeast cell cycle. Science 2005, 307: 724-727.
13. Duhamel, P., Vetterili, M. Fast Fourier transforms: a tutorial review and a state of the art. Signal Processing 1990, 19: 259-299.
14. Frigo, M., Johnson, S. G. FFTW: an adaptive software architecture of the FFT. Proc. Int. Conference on Acoustics, Speech, and Signal Processing 1998, 3: 1381-1384.
15. Dempster, A. P., Laird, N. M., Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. JRSS 1977, 39: 1-38.
16. Lo, Y. T., Mendell, N. R., Rubin, D. B. Testing the number of components in a normal mixture. Biometrika 2001, 88: 767-778.
17. Chen, H. F., Kalbfleisch, J. D. Testing for finite mixture model with two components. J. Royal Statistic. Soc. Series B 2004, 66: 95-115.
18. Celeux, G., Soromenho, G. An entropy criterion for assessing the number of clusters in a mixture model. J. Classification 1996, 13: 195-212.
19. Biernacki, C., Celeux, G., Govaert, G. An improvement of the NEC criterion for assessing the number of clusters in a mixture model source. Non-Linear Anal. 1999, 20: 267-272.
20. Kim, S., Imoto, S., Miyano, S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 2004, 75: 57-65.
21. Chen, J. Identification of significant periodic genes in microarray gene expression data. BMC Bioinformatics 2005, 6: 286.
22. Di Camillo, B., Sanchez-Cabo, F., Toffolo, G., Nair, S. K., Trajanoski, Z., Cobelli, C. A quantization method based on threshold optimization for microarray short time series. BMC Bioinformatics 2005, Suppl 4: S11.
23. Amato, R., Ciaramella, A., Deniskina, N., Del Mondo, C. Di Bernardo, D. Donalek, C., Longo, G., Mangano, G., Miele, G., Raiconi, G., Staiano, A., Tagliaferri, R. A multistep approach to time series analysis and gene expression clustering. Bioinformatics 2006, 22: 589-596.
24. Conesa, A., M. Nueda, J., Ferrer, A., Talon, M. maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 2006, 22: 1096-1102.
25. Cho, R. J., Campbell, M. J., Winzeler, E. A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T. G., Gabrielian, A. E., Landsman, D., Lockhart, D. J., Davis, R. W. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell 1998, 2: 65-73.