Laplace approximated EM microarray analysis: An empirical Bayes approach for comparative microarray experiments

Statistical Science

Volumn 25, Issue 3, 2010, Pages 388-407

  Bar, Haim ^a   Booth, James ^b   Schifano, Elizabeth ^c   Wells, Martin T ^a  

^a Department of Molecular Biology and Genetics   (United States)

^b Plant Science Building   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

EM algorithm; Empirical bayes; Laplace approximation; LEMMA; LIMMA; Linear mixed models; Local false discovery rate; Microarray analysis; Mixture model; Two groups model

Indexed keywords

EID: 78651404580     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/10-STS339     Document Type: Article

References (34)

1. ALLISON, D. B., CUI, X., PAGE, G. P. and SABRIPOUR, M. (2006). Microarray data analysis: From disarray to consolidation and consensus. Nat. Genet. 7 55-65.
2. ALON, U., BARKAI, N., NOTTERMAN, D. A., GISH, K., YBARRA, S., MACK, D. and LEVINE, A. J. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA 96 6745-6750.
3. BALDI, P. and LONG, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics 17 509-519.
4. BAR, H. and SCHIFANO, E. (2009). Lemma: Laplace approximated EM Microarray Analysis R package, Version 1.2-1.
5. BENJAMINI, Y. and HOCHBERG, Y. (1995). Controlling the false discovery rate-a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57 499-517.
6. BUTLER, R. W. (2007). Saddlepoint Approximations with Applications. Cambridge Univ. Press, Cambridge.
7. CALLOW, M. J., DUDOIT, S., GONG, E. L., SPEED, T. P. and RUBIN, E. M. (2000). Microarray expression profiling identifies genes with altered expression in HDL-deficient mice. Genome Res. 10 2022-2059.
8. CUI, X. and CHURCHILL, G. A. (2003). Statistical tests for differential expression in cDNA microarray experiments. Genome Biol. 4 210.
9. CUI, X., HWANG, J. T. G., QUI, J., BLADES, N. J. and CHURCHILL, G. A. (2005). Improved statistical tests for differential gene expression by shrinking variance components. Bio-statistics 6 59-75.
10. DE BRUIJN, N. G. (1981). Asymptotic Methods in Analysis. Dover, New York.
11. DO, K.-A., MÜLLER, P. and TANG, F. (2005). A Bayesian mixture model for differential gene expression. J. Roy. Statist. Soc. Ser. C 54 627-644.
12. EFRON, B. (2005). Local false discovery rates. Available at http://www-stat.stanford.edu/~ckirby/brad/papers/2005LocalFDR.pdf
13. EFRON, B. (2008). Microarrays, empirical Bayes and the two groups model. Statist. Sci. 23 1-22.
14. EFRON, B., TIBSHIRANI, R., STOREY, J. and TUSHER, V. (2001). Empirical Bayes analysis of microarray experiment. J. Amer. Statist. Assoc. 96 1151-1160.
15. EFRON, B., TURNBULL, B. B. and NARASIMHAN, B. (2008). locfdr: Computes local false discovery rates R package, Version 1.1-6.
16. FIGUEROA, M. E., REIMERS, M., THOMPSON, R. F., YE, K., LI, Y., SELZER, R. R., FRIDRIKSSON, J., PAIETTA, E., WIERNIK, P., GREEN, R. D., GREALLY, J. M. and MELNICK, A. (2008). An integrative genomic and epigenomic approach for the study of transcriptional regulation. PLoS ONE 3 e1882.
17. GENOVESE, C. and WASSERMAN, L. (2002). Operating characteristics and extensions of the false discovery rate procedure. J. Roy. Statist. Soc. Ser. B 64 499-517.
18. HONG, C. S. (2009). Optimal threshold from ROC and CAP curves. Comm. Statist. Simulation Comput. 38 2060-2072.
19. HWANG, J. T. G. and LIU, P. (2010). Optimal tests shrinking both means and variances applicable to microarray data. Stat. Appl. Genet. Mol. Biol. 9 article 36.
20. KENDZIORSKI, C. M., NEWTON, M. A., LAN, H. and GOULD, M. N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Stat. Med. 22 3899-3914.
21. KERR, M., MARTIN, M. and CHURCHILL, G. (2000). Analysis of variance in microarray data. J. Comput. Biol. 7 819-837.
22. LIU, P. (2006). Sample size calculation and empirical Bayes tests for microarray data, Ph.D. thesis, Cornell Univ.
23. LONNSTEDT, I., RIMINI, R. and NILSSON, P. (2005). Empirical Bayes microarray ANOVA and grouping cell lines by equal expression levels. Statist. Appl. Genet. Mol. Biol. 4 Article 7.
24. LONNSTEDT, I. and SPEED, T. (2002). Replicated microarray data. Statist. Sinica 12 31-46.
25. NEWTON, M. A., KENDZIORSKI, C. M., RICHMOND, C. S., BLATTNER, F. R. and TSUI, K. W. (2001). On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Comput. Biol. 8 37-52.
26. R DEVELOPMENT CORE TEAM (2007). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
27. SMYTH, G. K. (2004). Linear models for empirical Bayes methods for assessing differential expression in microarray experiments. Statist. Appl. Genet. Mol. Biol. 3 Article 3.
28. SMYTH, G. K. (2005). Limma: Linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor (R. Gentleman, V. Carey, S. Dudoit, R. Irizarry and W. Huber, eds.) 397-420. Springer, New York.
29. SU, Y., MURALI, T. M., PAVLOVIC , V., SCHAFFER, M. and KASIF, S. (2003). RankGene: Identification of diagnostic genes based on expression data. Bioinformatics 19 1578-1579.
30. TAI, Y. C. and SPEED, T. P. (2006). A multivariate empirical Bayes statistic for replicated microarray time course data. Ann. Statist. 34 2387-2412.
31. TAI, Y. C. and SPEED, T. P. (2009). On gene ranking using replicated microarray time course data. Biometrics 65 40-51.
32. TUSHER, V. G., TIBSHIRANI, R. and CHU, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98 5116-5121.
33. WRIGHT, G. W. and SIMON, R. M. (2003). A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 19 2448-2455.
34. ZHANG, M., ZHANG, D. and WELLS, M. T. (2010). Generalized thresholding estimators for high-dimensional location parameters. Statist. Sinica 20 911-926.