Bayesian ranking and selection methods using hierarchical mixture models in microarray studies

Biostatistics

Volumn 11, Issue 2, 2010, Pages 281-289

  Noma, Hisashi ^a   Matsui, Shigeyuki ^b   Omori, Takashi ^a   Sato, Tosiya ^a  

^a KYOTO UNIVERSITY SCHOOL OF PUBLIC HEALTH   (Japan)

^b INSTITUTE OF STATISTICAL MATHEMATICS   (Japan)

Author keywords

Empirical Bayes; Gene expression; Hierarchical mixture models; Microarrays; Ranking and selection

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; BIOMETRY; BREAST TUMOR; COMPUTER SIMULATION; DNA MICROARRAY; DOWN REGULATION; FEMALE; GENE EXPRESSION PROFILING; GENETICS; HUMAN; METABOLISM; METHODOLOGY; PROGNOSIS; RECURRENT DISEASE; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; STATISTICS; UPREGULATION;

ALGORITHMS; BAYES THEOREM; BIOMETRY; BREAST NEOPLASMS; COMPUTER SIMULATION; DOWN-REGULATION; FEMALE; GENE EXPRESSION PROFILING; HUMANS; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROGNOSIS; RECURRENCE; SENSITIVITY AND SPECIFICITY; UP-REGULATION;

EID: 77749264356     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxp047     Document Type: Article

References (24)

1. BERGER, J. O. (1985). Statistical Decision Theory and Bayesian Analysis. New York: Springer.
2. CARLIN, B. P. AND LOUIS, T. A. (2009). Bayesian Methods for Data Analysis. New York: Chapman & Hall.
3. CHOE, S. E., BOUTROS, M., MICHELSON, A. M., CHURCH, G. M. AND HALFON, M. S. (2005). Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defned control dataset. Genome Biology 6, R16.
4. DEMPSTER, A. P., LAIRD, N. M. AND RUBIN, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B 39, 1-38.
5. EFRON, B. (2008). Microarrays, empirical Bayes and the two-groups model (with discussion). Statistical Science 23, 1-47.
6. EFRON, B., TIBSHIRANI, R., STOREY, J. AND TUSHER, V. (2001). Empirical Bayes analysis of a microarray experiment. Journal of the American Statistical Association 96, 1151-1160.
7. GOTTARDO, R., PANNUCCI, J. A., KUSKE, C. R. AND BRETTIN, T. (2003). Statistical analysis of microarray data: a Bayesian approach. Biostatistics 4, 597-620.
8. GREENLAND, S. (1993). Methods for epidemiologic analyses of multiple exposures: a review and comparative study of maximum-likelihood, preliminary testing, and empirical-Bayes regression. Statistics in Medicine 12, 717-736.
9. GUO, L., LOBENHOFER, E. K., WANG, C., SHIPPY, R., HARRIS, S. C., ZHANG, L., MEI, N., CHEN, T., HERMAN, D., GOODSAID, F. M. and others (2006). Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotechnology 24, 1151-1161.
10. LAIRD, N. M. AND LOUIS, T. A. (1989). Empirical Bayes ranking methods. Journal of Educational Statistics 14, 29-46.
11. LIN, R., LOUIS, T. A., PADDOCK, S. M. AND RIDGEWAY, G. (2006). Loss function based ranking in two-stage, hierarchical models. Bayesian Analysis 1, 915-946.
12. LO, K. AND GOTTARDO, R. (2007). Flexible empirical Bayes models for differential gene expression. Bioinformatics 23, 328-335.
13. LÖ NNSTEDT, I. AND SPEED, T. P. (2002). Replicated microarray data. Statistica Sinica 12, 31-46.
14. LOUIS, T. A. AND SHEN, W. (1999). Innovations in Bayes and empirical Bayes methods: estimating parameters, populations and ranks. Statistics in Medicine 18, 2493-2505.
15. MAQC CONSORTIUM (2006). The MicroArray Quality Control (MAQC) project shows inter-and intraplatform reproducibility of gene expression measurements. Nature Biotechnology 24, 1151-1161.
16. MATSUI, S., ZENG, S., YAMANAKA, T. AND SHAUGHNESSY, J. (2008). Sample size calculations based on ranking and selection in microarray experiments. Biometrics 64, 217-226.
17. MCLACHLAN, G. J., BEAN, R. W. AND JONES, L. B.-T. (2006). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22, 1608-1615.
18. MCLACHLAN, G. J., DO, K.-A. AND AMBROISE, C. (2004). Analyzing Microarray Gene Expression Data. Hoboken, NJ: Wiley.
19. NEWTON, M. A. AND KENDSIORSKI, C. M. (2003). Parametric empirical Bayes methods for microarrays. In: Parmigiani, G., Garrett, E. S., Irizarry, R. A. and Zeger, S. (editors), The Analysis of Gene Expression Data: Methods and Software. New York: Springer, pp. 254-271.
20. 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. Biostatistics 8, 37-52.
21. NEWTON, M. A., NOUEIRY, A., SARKAR, D. AND AHLQUIST, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5, 155-176.
22. SHEN, W. AND LOUIS, T. A. (1998). Triple-goal estimates in two-stage hierarchical models. Journal of the Royal Statistical Society, Series B 60, 455-471.
23. STOREY, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B 64, 479-498.
24. WANG, Y., KLIJN, J. G., ZHANG, Y., SIEUWERTS, A. M., LOOK, M. P., YANG, F., TALANTOV, D., TIMMER-MANS, M., MEIJER-VAN GELDER, M. E., YU, J. and others (2005). Gene-expression profles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365, 671-679.