Hypothesis setting and order statistic for robust genomic meta-analysis

Annals of Applied Statistics

Volumn 8, Issue 2, 2014, Pages 777-800

  Song, Chi ^a   Tseng, George C ^a  

^a UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Genomics; Meta analysis; Order statistic; p value

Indexed keywords

EID: 84903775766     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/13-AOAS683     Document Type: Article

References (33)

1. BEGUM, F., GHOSH, D., TSENG, G. C. and FEINGOLD, E. (2012). Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res. 40 3777-3784.
2. BENJAMINI, Y. and HOCHBERG, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57 289-300. MR1325392
3. BENJAMINI, Y. andYEKUTIELI, D. (2001). The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29 1165-1188. MR1869245
4. BERGER, R. L. (1982). Multiparameter hypothesis testing and acceptance sampling. Technometrics 24 295-300. MR0687187
5. BERGER, R. L. and HSU, J. C. (1996). Bioequivalence trials, intersection-union tests and equivalence confidence sets. Statist. Sci. 11 283-319. MR1445984
6. BIRNBAUM, A. (1954). Combining independent tests of significance. J. Amer. Statist. Assoc. 49 559-574. MR0065101
7. COOPER, H. M., HEDGES, L. V. and VALENTINE, J. C. (2009). The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation, Thousand Oaks, CA.
8. ERICKSON, S., KIM, K. and ALLISON, D. B. (2009). Meta-Analysis and Combining Information in Genetics and Genomics. Chapman & Hall/CRC, London.
9. FISHER, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh.
10. HEDGES, L. V. and OLKIN, I. (1980). Vote-counting methods in research synthesis. Psychol. Bull. 88 359-369.
11. KANG, D. D., SIBILLE, E., KAMINSKI, N. and TSENG, G. C. (2012). MetaQC: Objective quality control and inclusion/exclusion criteria for genomic meta-analysis. Nucleic Acids Res. 40 e15.
12. LI, J. and TSENG, G. C. (2011). An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies. Ann. Appl. Stat. 5 994-1019. MR2840184
13. LITTELL, R. C. and FOLKS, J. L. (1971). Asymptotic optimality of Fisher's method of combining independent tests. J. Amer. Statist. Assoc. 66 802-806. MR0312634
14. LITTELL, R. C. and FOLKS, J. L. (1973). Asymptotic optimality of Fisher's method of combining independent tests. II. J. Amer. Statist. Assoc. 68 193-194. MR0375577
15. OWEN, A. B. (2009). Karl Pearson's meta-analysis revisited. Ann. Statist. 37 3867-3892. MR2572446
16. PARK, P. J., KONG, S. W., TEBALDI, T., LAI, W. R., KASIF, S. and KOHANE, I. S. (2009). Integration of heterogeneous expression data sets extends the role of the retinol pathway in diabetes and insulin resistance. Bioinformatics 25 3121-3127.
17. PEARSON, K. (1934). On a new method of determining "goodness of fit. " Biometrika 26 425-442.
18. QIU, X., YAKOVLEV, A. et al. (2006). Some comments on instability of false discovery rate estimation. J. Bioinform. Comput. Biol. 4 1057-1068.
19. RHODES, D. R., BARRETTE, T. R., RUBIN, M. A., GHOSH, D. and CHINNAIYAN, A. M. (2002). Meta-analysis of microarrays: Interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res. 62 4427-4433.
20. ROY, S. N. (1953). On a heuristic method of test construction and its use in multivariate analysis. Ann. Math. Stat. 24 220-238. MR0057519
21. SHEN, K. and TSENG, G. C. (2010). Meta-analysis for pathway enrichment analysis when combining multiple genomic studies. Bioinformatics 26 1316-1323.
22. SONG, C. and TSENG, G. C. (2014a). Supplement to "Hypothesis setting and order statistic for robust genomic meta-analysis. " DOI:10.1214/13-AOAS683SUPPA.
23. SONG, C. and TSENG, G. C. (2014b). Supplement to "Hypothesis setting and order statistic for robust genomic meta-analysis. " DOI:10.1214/13-AOAS683SUPPB.
24. SONG, C. and TSENG, G. C. (2014c). Supplement to "Hypothesis setting and order statistic for robust genomic meta-analysis. " DOI:10.1214/13-AOAS683SUPPC.
25. SONG, C. and TSENG, G. C. (2014d). Supplement to "Hypothesis setting and order statistic for robust genomic meta-analysis. " DOI:10.1214/13-AOAS683SUPPD.
26. STOUFFER, S. A., SUCHMAN, E. A., DEVINNEY, L. C., STAR, S. A. andWILLIAMS JR., R. M. (1949). The American Soldier: Adjustment During Army Life. Princeton Univ. Press, Princeton, NJ.
27. SUBRAMANIAN, A., TAMAYO, P., MOOTHA, V. K., MUKHERJEE, S., EBERT, B. L., GILLETTE, M. A., PAULOVICH, A., POMEROY, S. L., GOLUB, T. R., LANDER, E. S. et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genomewide expression profiles. Proc. Natl. Acad. Sci. USA 102 15545-15550.
28. TIBSHIRANI, R., WALTHER, G. and HASTIE, T. (2001). Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B Stat. Methodol. 63 411-423. MR1841503
29. TIPPETT, L. H. C. (1931). The Methods of Statistics. Williams Norgate, London.
30. TSENG, G. C., GHOSH, D. and FEINGOLD, E. (2012). Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res. 40 3785-3799.
31. WANG, X., LIN, Y., SONG, C., SIBILLE, E. and TSENG, G. C. (2012a). Detecting diseaseassociated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder. BMC Bioinformatics 13 52.
32. WANG, X., KANG, D. D., SHEN, K., SONG, C., LU, S., CHANG, L. C., LIAO, S. G., HUO, Z., TANG, S., KAMINSKI, N. et al. (2012b). An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 28 2534-2536.
33. WILKINSON, B. (1951). A statistical consideration in psychological research. Psychol. Bull. 48 156-158.