A bootstrap test for the analysis of microarray experiments with a very small number of replications

Applied Bioinformatics

Volumn 5, Issue 3, 2006, Pages 173-179

  Neuhäuser, Markus ^a   Jöckel, Karl Heinz ^a  

^a UNIVERSITY OF DUISBURG ESSEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DNA MICROARRAY; GENE EXPRESSION; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MICROARRAY ANALYSIS; SAMPLE SIZE; SIMULATION;

ALGORITHMS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DNA; DNA REPLICATION; GENE EXPRESSION PROFILING; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SAMPLE SIZE;

EID: 33747812876     PISSN: 11755636     EISSN: 11755636     Source Type: Journal    
DOI: 10.2165/00822942-200605030-00005     Document Type: Article

References (34)

1. Gadbury GL, Page GP, Heo M, et al. Randomization tests for small samples: an application for genetic expression data. Appl Stat 2003; 52: 365-76
2. Hunter L, Taylor RC, Leach SM, et al. GEST: a gene expression search tool based on a novel Bayesian similarity metric. Bioinformatics 2001; 17 Suppl. 1: S115-22
3. Thomas JG, Olson JM, Tapscott SJ, et al. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 2001; 11: 1227-36
4. Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 2002; 18: 546-54
5. Craig BA, Black MA, Doerge RW. Gene expression data: the technology and statistical analysis. J Agric Biol Environ Stat 2003; 8: 1-28
6. Giles PJ, Kipling D. Normality of oligonucleotide microarray data and implications for parametric statistical analyses. Bioinformatics 2003; 19: 2254-62
7. Liu L, Hawkins DM, Ghosh S, et al. Robust singular value decomposition analysis of microarray data. Proc Natl Acad Sci U S A 2003; 100: 13167-72
8. Neuhäuser M, Senske R. The Baumgartner-Weiß-Schindler test for the detection of differentially expressed genes in replicated microarray experiments. Bioinformatics 2004; 20: 3553-64
9. Zhao Y, Pan W. Modified nonparametric approaches to detecting differentially expressed genes in replicated microarray experiments. Bioinformatics 2003; 19: 1046-54
10. Kayo T, Allison DB, Weindruch R, et al. Influences of aging and caloric restriction on the transcriptional profile of skeletal muscle from rhesus monkeys. Proc Natl Acad Sci U S A 2001; 98: 5093-8
11. Beasley TM, Page GP, Brand JPL, et al. Chebyshev's inequality for nonparametric testing with small N and α in microarray research. Appl Stat 2004; 53: 95-108
12. Hotelling H. The behavior of some standard statistical tests under non-normal conditions. In: Neyman J, editor. Proc. 4th Berkeley Symp. Mathematical Statistics and Probability. Vol. 1. Berkeley (CA): University of California Press, 1961: 319-59
13. Bradley JV. Distribution-free statistical tests. Englewood Cliffs (NJ): Prentice Hall, 1968
14. Zimmerman DW, Zumbo BD. The relative power of parametric and nonparametric statistical methods. In: Keren G, Lewis C, editors. A handbook for data analysis in the behavioral sciences: methodological issues. Hillsdale (NJ): Lawrence Erlbaum Associates, 1993: 481-517
15. Blair RC, Higgins JJ, Smitley DS. On the relative power of the U and t tests. Br J Math Stat Psychol 1980; 33: 114-20
16. Fisher RA. The comparison of samples with possibly unequal variences. Ann Eugen 1939; 9: 174-80
17. Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman & Hall, 1993
18. Hall P, Wilson SR. Two guidelines for bootstrap hypothesis testing. Biometrics 1991; 47: 757-62
19. Westfall PH, Young SS. Resampling-based multiple testing. New York: Wiley, 1993
20. Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001; 17: 509-19
21. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 2001; 98: 5116-21
22. Cui X, Hwang JTG, Qiu J, et al. Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 2005; 6: 59-75
23. Allison DB, Cui X, Page GP, et al. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006; 7: 55-65
24. Rózsa L, Reiczigel J, Majoros G. Quantifying parasites in samples of hosts. J Parasitol 2000; 86: 228-32
25. Wilcox RR. Applying contemporary statistical techniques. San Diego (CA): Elsevier Academic Press, 2003
26. Manly BFJ. Randomization, bootstrap and Monte Carlo methods in biology. 2nd ed. London: Chapman & Hall, 1997
27. Pitman EJG. Significance tests which may be applied to samples from any populations. Supplement to The Journal of the Royal Statistical Society 1937; 4: 119-30
28. Neuhäuser M, Manly BF. The Fisher-Pitman permutation test when testing for differences in mean and variance. Psychol Rep 1994 Feb; 94 (1): 189-94
29. Janssen A, Pauls T. How do bootstrap and permutation tests work? Ann Stat 2003; 31: 768-806
30. Reiczigel J, Zakarias I, Rózsa L. A bootstrap test of stochastic equality of two populations. Am Stat 2005; 59: 156-61
31. Lyons-Weiler J, Patel S, Becich MJ, et al. Tests for finding complex patterns of differential expression in cancers: towards individualized medicine. BMC Bioinformatics 2004; 5: 110
32. Zaykin DV, Zhivotovsky LA, Westfall PH, et al. Truncated product method for combining p-values. Genet Epidemiol 2002; 22: 170-85
33. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 2003; 100: 9440-5
34. Dudbridge F, Koeleman BPC. Rank truncated product of p-values, with application to genomewide association scans. Genet Epidemiol 2003; 25: 360-6