Nonparametric variance estimation in the analysis of microarray data: A measurement error approach

Biometrika

Volumn 95, Issue 2, 2008, Pages 437-449

  Carroll, Raymond J ^a   Wang, Yuedong ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Heteroscedasticity; Local polynomial regression; Measurement error; Microarray; Nonparametric regression; Permutation; Simex; Simulation extrapolation; Variance function estimation

Indexed keywords

EID: 44849093556     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asn017     Document Type: Article

References (23)

1. ALON, U., BARKAI, N., NOTTERMAN, D., GISH, K., YBARRA, S., MACK, D. & LEVINE, A. J. (1999). Broad patterns of gene expression revealed by clustering of tumour and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. 96, 6745-50.
2. BALDI, P. & LONG, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 17, 509-19.
3. CALLOW, M. J., DUDOIT, S., GONG, E. L., SPEED, T. P. & RUBIN, E. M. (2000). Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res. 10, 2022-9.
4. CARROLL, R. J., MACA, J. D. & RUPPERT, D. (1999). Nonparametric regression in the presence of measurement error. Biometrika 86, 541-54.
5. CARROLL, R. J., RUPPERT, D. & WELSH, A. H. (1998). Local estimating equations. J. Am. Statist. Assoc. 93, 214-27.
6. CARROLL, R. J., RUPPERT, D., STEFANSKI, L. A. & CRAINICEANU, C. (2006). Measurement Error in Nonlinear Models: A Modern Perspective, 2nd ed. New York: Chapman and Hall.
7. CHEN, Y., DOUGHERTY, E. R. & BITTNER, M. L. (1997). Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Optics 2, 364-74.
8. CHEN, Y., KAMAT, V., DOUGHERTY, E. R., BITTNER, M. L., MELTZER, P. S. & TRENT, J. M. (2002). Ratio statistics of gene expression levels and applications to microarray data analysis. Bioinformatics 18, 1207-15.
9. COOK, J. & STEFANSKI, L. A. (1995). A simulation extrapolation method for parametric measurement error models. J. Am. Statist. Assoc. 89, 1314-28.
10. CUI, X., HWANG, J. T. G., QIU, J., BLADES, N. J. & CHURCHILL, G. A. (2005). Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 6, 59-75.
11. DEVANARAYAN, V. & STEFANSKI, L. A. (2002). Empirical simulation extrapolation for measurement error models with replicate measurements. Statist. Prob. Lett. 59, 219-25.
12. HUANG, X. & PAN, W. (2002). Comparing three methods for variance estimation with duplicated high density oligonu-cleotide arrays. Funct. Integr. Genomics 2, 126-33.
13. JAIN, N., THATTE, J., BRACIALE, T., LEY, K., O'CONNELL, M. & LEE, J. (2003). Local-pooled error test for identifying differentially expressed genes with a small number of replicated microarrays. Bioinformatics 19, 1945-51.
14. KAMB, A. & RAMASWAMI, A. (2001). A simple method for statistical analysis of intensity differences in microarray-derived gene expression data. BMC Biotechnol. 1, 1-8.
15. LEUNG, Y. & CAVALIERI, D. (2003). Fundamentals of cDNA microarray data analysis. Trends Genet. 11, 649-59.
16. LIN, Y., NADLER, S. T., ATTIE, A. D. & YANDELL, B. S. (2003). Adaptive gene picking with microarray data: detecting important low abundance signals. In The Analysis of Gene Expression Data: Methods and Software, Ed. G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger, pp. 291-312. New York: Springer.
17. LÖNNSTEDT, I. & SPEED, T. (2002). Replicated microarray data. Statist. Sinica 12, 31-46.
18. NGUYEN, D. V., ARPAT, A. B., WANG, N. & CARROLL, R. J. (2002). DNA microarray experiments: biological and technological aspects. Biometrics 58 701-17.
19. ROCKE, D. M. & DURBIN, B. (2001). A model for measurement error for gene expression arrays. J. Comp. Biol. 8, 557-69.
20. RUPPERT, D., WAND, M. & CARROLL, R. (2003). Semiparametric Regression. New York: Cambridge University Press.
21. SMYTH, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statist. Appl. Genet. Mol. Biol. 3, Article 3.
22. TONG, T. & WANG, Y. (2007). Optimal shrinkage estimation of variances with applications to microarray data analysis. J. Am. Statist. Assoc. 102, 113-22.
23. WENG, L., DAI, H., ZHAN, Y., HE, Y., STEPANIANTS, S. B. & BASSETT, D. E. (2006). Rosetta error model for gene expression analysis. Bioinformatics 22, 1111-21.