Flexible temporal expression profile modelling using the Gaussian process

Computational Statistics and Data Analysis

Volumn 51, Issue 3, 2006, Pages 1754-1764

  Yuan, Ming ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Differential expression; Gaussian process; Temporal gene expression

Indexed keywords

COMPUTER SIMULATION; DISEASES; HYDROGEN PEROXIDE; OXIDATION; TIME SERIES ANALYSIS;

DIFFERENTIAL EXPRESSION; GAUSSIAN PROCESS; TEMPORAL GENE EXPRESSION;

GENES;

EID: 33750683623     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2005.11.017     Document Type: Article

References (19)

1. Benjamini Y., and Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57 (1995) 289-300
2. Berger J.O. Statistical Decision Theory and Bayesian Analysis (1985), Springer, New York
3. Chuang Y., Chen Y., Gadisetti V., et al. Gene expression after treatment with hydrogen peroxide, menadione, or t-butyl hydroperoxide in breast cancer cells. Cancer Res. 62 (2002) 6246-6254
4. Cressie N. Statistics for Spatial Data (1993), Wiley, London
5. Edwards M.G., Sarkar D., Klopp R., Morrow J.D., Weindruch R., and Prolla T.A. Age-related impairment of the transcriptional response to oxidative stress in the mouse heart. Physiol Genomics 13 (2003) 119-127
6. Guo X., Qi H., Verfaillie C., and Pan W. Statistical significance analysis of longitudinal gene expression data. Bioinformatics 19 (2003) 1628-1635
7. Heagerty P., Liang K., Zeger S., and Diggle P. Analysis of Longitudinal Data. second ed (2004), Springer, New York
8. Hong, F., Li, H., 2004. Function empirical Bayes methods for identify genes with different time course expression profiles. Technical Report, Center for Bioinformatics and Molecular Biostatistics, University of California.
9. Kendziorski C.M., Newton M.A., Lan H., and Gould M.N. On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statist. Medicine 22 (2003) 3899-3914
10. Newton M.A., Kendziorski C.M., Richmond C.S., Blattner F.R., and Tsui K.W. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J. Comput. Biol. 8 (2001) 37-52
11. Newton M.A., Noueiry A., Sarkar D., and Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 (2004) 155-176
12. Park T., Yi S., Lee S., Lee S., Yoo D., Ahn J., and Lee Y. Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 19 (2003) 694-703
13. Parmigiani G., Garrett E.S., Irizarry R., and Zeger S.L. The Analysis of Gene Expression Data: Methods and Software (2003), Springer, New York
14. Peddada S., Lobenhofer E., Li L., Alshari C., Weinberg C., and Umbach D. Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 19 (2003) 834-841
15. Stein M.L. Statistical Interpolation of Spatial Data: Some Theory for Kriging (1999), Springer, New York
16. Storey, J., Leek, J., Xiao, W., Dai, J., Davis, R., 2004. A significant method for time course microarray experiments applied to two human studies. Technical Report, Department of Biostatistics, University of Washington.
17. Wahba G. Spline Models for Observational Data (1990), SIAM, Philadelphia
18. Xu X., Olson J., and Zhao L. A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model. Human Molecular Genetics 11 (2002) 1977-1985
19. Yuan, M., Kendziorski, C., 2006. Hidden Markov models for microarray time course data in multiple biological conditions (with discussion), J. Amer. Statist. Assoc., to appear.