Identifying temporally differentially expressed genes through functional principal components analysis

Biostatistics

Volumn 10, Issue 4, 2009, Pages 667-679

  Liu, Xueli ^a   Yang, Mark C K ^b  

^a Division of Biostatistics   (United States)

^b University of Florida   (United States)

Author keywords

False discovery rate; Functional hypothesis testing; Functional principal components analysis; Permutation test; Time course gene expression profiles

Indexed keywords

ANALYSIS OF VARIANCE; ARTICLE; BIOSTATISTICS; BLOOD; BLOOD CELL; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; METHODOLOGY; MULTIPLE SCLEROSIS; PRINCIPAL COMPONENT ANALYSIS; STATISTICS; TIME;

ANALYSIS OF VARIANCE; BIOSTATISTICS; BLOOD CELLS; GENE EXPRESSION PROFILING; HUMANS; MULTIPLE SCLEROSIS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; TIME FACTORS;

EID: 74349089785     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxp022     Document Type: Article

References (23)

1. BAR-JOSEPH, Z., GERBER, G., SIMON, I., GIFFORD, D. K. AND JAAKKOLA, T. S. (2003). Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. Proceedings of the National Academy of Sciences USA 100, 10146-10151.
2. BENJAMINI, Y. AND HOCHBERG, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289-300.
3. DIGGLE, P. J. AND AL WASEL, I. (1997). Spectral analysis of replicated biomedical time series. Applied Statistics 46, 31-71.
4. DIGGLE, P. J., LIANG, K. Y. AND ZEGER, S. L. (1994). Analysis of Longitudinal Data. Oxford: Clarendon.
5. EFRON, B., TIBSHIRANI, R., GOSS, V. AND CHU, G. (2001). Microarrays and their use in a comparative experiment. Journal of the American Statistical Association 96, 1151-1160.
6. EISEN, M. B., SPELLMAN, P. T., BROWN, P. O. AND BOTSTEIN, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences USA 95, 14863-14868.
7. FERNALD, G. H., KNOTT, S., PACHNER, A., CAILLIER, S. J., NARAYAN, K., OKSENBERG, J. R., MOUSAVI, P. AND BARANZINI, S. E. (2007). Genome-wide network analysis reveals the global properties of IFN-β immediate transcriptional effects in humans. Journal of Immunology 178, 5076-5085.
8. HONG, F. AND LI, H. (2006). Functional hierarchical models for identifying genes with different time-course expression profiles. Biometrics 62, 534-544.
9. JAMES, G., HASTIE, T. G. AND SUGAR, C. A. (2001). Principal component models for sparse functional data. Biometrika 87, 587-602.
10. JAMES, G. AND SUGAR, C. A. (2003). Clustering for sparsely sampled functional data. Journal of the American Statistical Association 98, 397-408.
11. KERR, M. K., MARTIN, M. AND CHURCHILL, G. A. (2000). Analysis of variance for gene expression microarray data. Journal of Computational Biology 7, 819-837.
12. LAIRD, N. M. AND WARE, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963-974.
13. LIU, X. AND MÜ LLER, H. G. (2003). Modes and clustering for time-warped gene expression profile data. Bioinfor-matics 19, 1937-1944.
14. MÜ LLER, H. G., STADTMÜ LLER, U. AND YAO, F. (2006). The functional variance process. Journal of the American Statistical Association 101, 1007-1018.
15. RAMSAY, J. O. AND SILVERMAN, B. W. (1997). Functional Data Analysis. New York: Springer.
16. RICE, J. AND WU, C. (2000). Nonparametric mixed effects models for unequally sampled noisy curves. Biometrics 57, 253-259.
17. SHEN, Q. AND FARAWAY, J. (2004). An F test for linear models with functional responses. Statistica Sinica 14, 1239-1257.
18. STARK, G. R., KERR, I. M., WILLIAMS, B. R., SILVERMAN, R. H. AND SCHREIBER, R. D. (1998). How cells responde to interferrons. Annual Review of Biochemistry 67, 227-264.
19. STOREY, J., XIAO, W., LEEK, J. T., DAI, J. Y. AND DAVIS, R. W. (2005). A significance method for time course microarray experiments applied to two human studies. Proceedings of the National Academy of Sciences USA 102, 12837-12842.
20. STOREY, J. D. AND TIBSHIRANI, R. (2003). Statistical significance for genome-wide studies. Proceedings of the National Academy of Sciences USA 100, 9440-9445.
21. WANG, J. AND KIM, S. (2003). Global analysis of dauer gene expression in Caenorhabditis elegans. Development 130, 1621-1634.
22. YAO, F., MÜ LLER, H. G. AND WANG, J.-L. (2005). Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association 100, 577-590.
23. YUAN, M. AND KENDZIORSKI, C. (2006). Hidden Markov model for microarray time course data in multiple biological conditions. Journal of the American Statistical Association 101, 1323-1340.