Sequential monte carlo multiple testing

Bioinformatics

Volumn 27, Issue 23, 2011, Pages 3235-3241

  Sandve, Geir Kjetil ^a   Ferkingstad, Egil ^b   Nygård, Ståle ^c  

^a UNIVERSITY OF OSLO   (Norway)

^b NORWEGIAN COMPUTING CENTER   (Norway)

^c OSLO UNIVERSITY HOSPITAL   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; COMPUTER SIMULATION; GENETIC ASSOCIATION; GENOMICS; HISTONE CODE; METHODOLOGY; MONTE CARLO METHOD;

ALGORITHMS; COMPUTER SIMULATION; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HISTONE CODE; MONTE CARLO METHOD;

EID: 82255194268     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btr568     Document Type: Article

References (36)

1. Barski, A. et al. (2007) High-resolution profiling of histone methylations in the human genome. Cell, 129, 823-837.
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.
3. Besag, J. and Clifford, P. (1991) Sequential Monte Carlo p-values. Biometrika, 78, 301.
4. Celisse, A. and Robin, S. (2010) A cross-validation based estimation of the proportion of true null hypotheses. J. Stat. Plan. Inf., 140, 3132-3147.
5. Davison, A. and Hinkley, D. (1997) Bootstrap Methods and their Application. Cambridge University Press, Cambridge, UK.
6. Efron, B. (2004) Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J. Am. Stat. Assoc., 99, 96-104.
7. Ewan Birney, J. et al. (2007) Identification and analysis of functional elements in 1% of the human genome by the encode pilot project. Nature, 447, 799-816.
8. Finner, H. and Gontscharuk, V. (2009) Controlling the familywise error rate with plugin estimator for the proportion of true null hypotheses. J. R. Stat. Soc. Ser. B, 71, 1031-1048.
9. Friguet, C. and Causeur, D. (2011) Estimation of the proportion of true null hypotheses in high-dimensional data under dependence. Comput. Stat. Data Anal., 55, 2665-2676.
10. Gandy, A. (2009) Sequential implementation of Monte Carlo tests with uniformly bounded resampling risk. J. Am. Stat. Assoc., 104, 1504-1511.
11. Goecks, J. et al. (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol., 11, R86.
12. Hammersley, J. and Morton, K. (1954) Poor man's Monte Carlo. J. R. Stat. Soc. Ser. B, 16, 23-38.
13. Hope, A. (1968) A simplified Monte Carlo significance test procedure. J. R. Stat. Soc. Ser. B, 30, 582-598.
14. Horner, D.S. et al. (2010) Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing. Brief. Bioinformatics, 11, 181-197.
15. Hwang, Y. (2011) Comparisons of estimators of the number of true null hypotheses and adaptive FDR procedures in multiplicity testing. J. Stat. Comput. Simul., 81, 207-220.
16. Jiang, H. and Doerge, R. (2008) Estimating the proportion of true null hypotheses for multiple comparisons. Cancer Informat., 6, 25-32.
17. Kustra, R. et al. (2008) Efficient p-value estimation in massively parallel testing problems. Biostatistics, 9, 601.
18. Langaas, M. et al. (2005) Estimating the proportion of true null hypotheses, with application to DNA microarray data. J. R. Stat. Soc. Ser. B, 67, 555-572.
19. Lin, D. (2005) An efficient Monte Carlo approach to assessing statistical significance in genomic studies. Bioinformatics, 21, 781-787.
20. McPherson, J. (2009) Next-generation gap. Nat. Methods, 6, S2-S5.
21. Nettleton, D. et al. (2006) Estimating the number of true null hypotheses from a histogram of p values. J. Agri. Biol. Environ. Stat., 11, 337-356.
22. North, B. et al. (2002)Anote on the calculation of empirical p values from Monte Carlo procedures. Am. J. Hum. Genet., 71, 439.
23. Owen, A. (2005) Variance of the number of false discoveries. J. R. Stat. Soc. Ser. B, 67, 411-426.
24. Pauler, F.M. et al. (2009) H3K27me3 forms BLOCs over silent genes and intergenic regions and specifies a histone banding pattern on a mouse autosomal chromosome. Genome Research, 19, 221-233.
25. Pekowska, A. et al. (2010) A unique H3K4me2 profile marks tissue-specific gene regulation. Genome Res., 20, 1493-1502.
26. Phipson, B. and Smyth, G. (2010) Permutation p-values should never be zero: Calculating exact p-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol., 9, 39.
27. Pounds, S. and Cheng, C. (2006) Robust estimation of the false discovery rate. Bioinformatics, 22, 1979-1987.
28. Pounds, S. and Morris, S. (2003) Estimating the occurrence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics, 19, 1236-1242.
29. Sandve, G.K. et al. (2010) The Genomic HyperBrowser: inferential genomics at the sequence level. Genome Biol., 11, R121.
30. Schweder, T. and Spjøtvoll, E. (1982) Plots of p-values to evaluate many tests simultaneously. Biometrika, 69, 493-502.
31. Seaman, S. and Müller-Myhsok, B. (2005) Rapid simulation of p values for product methods and multiple-testing adjustment in association studies. Am. J. Hum. Genet., 76, 399-408.
32. Shendure, J. and Ji, H. (2008) Next-generation DNA sequencing. Nat. Biotechnol., 26, 1135-1145.
33. Storey, J. (2002) A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B., 64, 479-498.
34. Tamhane, A.C. and Shi, J. (2009) Parametric mixture models for estimating the proportion of true null hypotheses and adaptive control of FDR. Lect. Notes Monograph Ser., 57, 304-325.
35. Wang, Z. et al. (2008) Combinatorial patterns of histone acetylations and methylations in the human genome. Nat. Genet., 40, 897-903.
36. Zhang, S. (2011) Towards accurate estimation of the proportion of true null hypotheses in multiple testing. PLoS One, 6, e18874.