The beta-binomial distribution for estimating the number of false rejections in microarray gene expression studies

Computational Statistics and Data Analysis

Volumn 53, Issue 5, 2009, Pages 1688-1700

  Hunt, Daniel L ^a   Cheng, Cheng ^a   Pounds, Stanley ^a  

^a ST JUDE CHILDREN S RESEARCH HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOACTIVITY; GENE EXPRESSION; GENES; PARAMETER ESTIMATION;

BINOMIAL DISTRIBUTIONS; DIFFERENTIAL EXPRESSIONS; EXPRESSED GENES; FALSE DISCOVERY RATES; FALSE REJECTIONS; GENE EXPRESSION LEVELS; INDEPENDENCE ASSUMPTIONS; MICROARRAY DATUM; MICROARRAY GENE EXPRESSIONS; NULL HYPOTHESIS; SIMULATION STUDIES; SOFT-TISSUE SARCOMATA; TISSUE SAMPLES;

ESTIMATION;

EID: 60349116028     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.01.013     Document Type: Article

References (19)

1. Allison D.B., Gadbury G.L., Moonseong H., Fernandez J.R., Lee C., Prolla T.A., and Weindruch R. A mixture model approach for the analysis of microarray gene expression data. Comput. Statist. Data Anal. 39 (2002) 1-20
2. 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 1 (1995) 289-300
3. Benjamini, Y., Hochberg, Y., Kling, Y., 1997. False discovery rate control in multiple hypotheses testing using dependent test statistics. Research Paper 97-1, Dept. of Statistics and Operations Research, Tel Aviv Univ
4. Benjamini Y., and Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29 4 (2001) 1165-1188
5. Cheng C., and Pounds S. False discovery rate paradigms for statistical analyses of microarray gene expression data. Bioinformation 1 10 (2007) 436-446
6. Cheng C., Pounds S., Boyett J.M., Pei D., Kuo M.-L., and Roussel M.F. Statistical significance threshold criteria for analysis of microarray gene expression data. Statist. Appl. Genet. Mol. Biol. 3 1 (2004) Article 36
7. Hsueh H., Chen J.J., and Kodell R.L. Comparison of methods for estimating number of true null hypotheses in multiplicity testing. J. Biopharm. Statist. 13 4 (2003) 675-689
8. Lesnoff, M., Lancelot, R., 2005. aod: Analysis of overdispersed data R package version 1. 1-8 http://cran.r-project.org/
9. Mehta T., Tanik M., and Allison D.B. Towards sound epistemological foundation of statistical methods for high-dimensional biology. Nature Genet. 36 9 (2004) 943-947
10. Pounds S. Estimation and control of multiple testing error rates for microarray studies. Briefings in Bioinformatics 7 1 (2006) 25-36
11. Pounds S., and Cheng C. Improving false discovery rate estimation. Bioinformatics 20 11 (2004) 1737-1745
12. Pounds S., and Morris S.W. Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19 10 (2003) 1236-1242
13. Somerville, P.N., 2004. FDR step-down and step-up procedures for the correlated case. In: IMS Lecture Notes Monograph Series. pp. 100-118
14. Storey J.D. A direct approach to false discovery rates. J. Roy. Statist. Soc. Ser. B 64 3 (2002) 479-498
15. Storey J.D., Taylor J.E., and Siegmund D. Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. J. Roy. Statist. Soc. Ser. B 66 1 (2004) 187-205
16. Storey J.D., and Tibshirani R. Statistical significance for genomewide studies. PNAS 100 16 (2003) 9440-9445
17. Tsai C.-A., Hsueh H.-M., and Chen J.J. Estimation of false discovery rates in multiple testing: Application to gene microarray data. Biometrics 59 (2003) 1071-1081
18. Tsai C.-A., Wang S.-J., Chen D.-T., and Chen J.J. Sample size for gene expression microarray experiments. Bioinformatics 21 8 (2005) 1502-1508
19. Yoon S.S., Segal N.H., Park P.J., Detwiller K.Y., Fernando N.T., Ryeom S.W., Brennan M.F., and Singer S. Angiogenic profile of soft tissue sarcomas based on analysis of circulating factors and microarray gene expression. J. Surg. Res. 135 (2006) 282-29