Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 610297, 11 pages
http://dx.doi.org/10.1155/2013/610297
Research Article

Power and Stability Properties of Resampling-Based Multiple Testing Procedures with Applications to Gene Oncology Studies

1Department of Public Health Sciences, Office of Public Health Studies, The University of Hawaii at Manoa, 1960 East-West Road, Honolulu, HI 96822, USA
2Department of Obstetrics, Gynecology, and Women’s Health, John A. Burns School of Medicine, University of Hawaii, 651 Ilalo Street, Honolulu, HI 96813, USA

Received 10 August 2013; Revised 14 October 2013; Accepted 18 October 2013

Academic Editor: Ao Yuan

Copyright © 2013 Dongmei Li and Timothy D. Dye. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. A. Kulesh, D. R. Clive, D. S. Zarlenga, and J. J. Greene, “Identification of interferon-modulated proliferation-related cDNA sequences,” Proceedings of the National Academy of Sciences of the United States of America, vol. 84, no. 23, pp. 8453–8457, 1987. View at Google Scholar · View at Scopus
  2. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative monitoring of gene expression patterns with a complementary DNA microarray,” Science, vol. 270, no. 5235, pp. 467–470, 1995. View at Google Scholar · View at Scopus
  3. D. A. Lashkari, J. L. Derisi, J. H. Mccusker et al., “Yeast microarrays for genome wide parallel genetic and gene expression analysis,” Proceedings of the National Academy of Sciences of the United States of America, vol. 94, no. 24, pp. 13057–13062, 1997. View at Publisher · View at Google Scholar · View at Scopus
  4. J. R. Pollack, C. M. Perou, A. A. Alizadeh et al., “Genome-wide analysis of DNA copy-number changes using cDNA microarrays,” Nature Genetics, vol. 23, no. 1, pp. 41–46, 1999. View at Publisher · View at Google Scholar · View at Scopus
  5. M. J. Buck and J. D. Lieb, “ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments,” Genomics, vol. 83, no. 3, pp. 349–360, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. R. Mei, P. C. Galipeau, C. Prass et al., “Genome-wide detection of allelic imbalance using human SNPs and high-density DNA arrays,” Genome Research, vol. 10, no. 8, pp. 1126–1137, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Y. Hehir-Kwa, M. Egmont-Petersen, I. M. Janssen, D. Smeets, A. G. van Kessel, and J. A. Veltman, “Genome-wide copy number profiling on high-density bacterial artificial chromosomes, single-nucleotide polymorphisms, and oligonucleotide microarrays: a platform comparison based on statistical power analysis,” DNA Research, vol. 14, no. 1, pp. 1–11, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Hochberg and A. C. Tamhane, Multiple Comparison Procedures, John Wiley & Sons, New York, NY, USA, 1987.
  9. J. P. Shaffer, “Multiple hypothesis testing: a review,” Annual Review of Psychology, vol. 46, pp. 561–584, 1995. View at Publisher · View at Google Scholar
  10. Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society B, vol. 57, no. 1, pp. 289–300, 1995. View at Google Scholar
  11. B. Efron, “Bootstrap methods: another look at the jackknife,” The Annals of Statistics, vol. 7, no. 1, pp. 1–26, 1979. View at Publisher · View at Google Scholar
  12. B. Efron and R. Tibshirani, An Introduction to the Bootstrap, CRC Press, New York, NY, USA, 1994.
  13. D. A. Freedman, “Bootstrapping regression models,” The Annals of Statistics, vol. 9, no. 6, pp. 1218–1228, 1981. View at Publisher · View at Google Scholar
  14. P. Hall, “On the bootstrap and confidence intervals,” The Annals of Statistics, vol. 14, no. 4, pp. 1431–1452, 1986. View at Publisher · View at Google Scholar
  15. K. S. Pollard and M. K. van der Laan, “Choice of a null distribution in resampling-based multiple testing,” Journal of Statistical Planning and Inference, vol. 125, no. 1-2, pp. 85–100, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. P. H. Westfall and S. S. Young, Resampling-Based Multiple Testing: Examples and Methods for P-Value Adjustment, John Wiley & Sons, New York, NY, USA, 1993.
  17. V. G. Tusher, R. Tibshirani, and G. Chu, “Significance analysis of microarrays applied to the ionizing radiation response,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 9, pp. 5116–5121, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Ge, S. Dudoit, and T. P. Speed, “Resampling-based multiple testing for microarray data analysis,” Test, vol. 12, no. 1, pp. 1–77, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. D. Rubin, S. Dudoit, and M. van der Laan, “A method to increase the power of multiple testing procedures through sample splitting,” Statistical Applications in Genetics and Molecular Biology, vol. 5, no. 1, article 19, 2006. View at Google Scholar · View at Scopus
  20. A. Jemal, R. Siegel, E. Ward et al., “Cancer statistics, 2006,” CA: A Cancer Journal for Clinicians, vol. 56, no. 2, pp. 106–130, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. C. S. Moreno, L. Matyunina, E. B. Dickerson et al., “Evidence that p53-mediated cell-cycle-arrest inhibits chemotherapeutic treatment of ovarian carcinomas,” PLoS ONE, vol. 2, no. 5, article e441, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Edgar, M. Domrachev, and A. E. Lash, “Gene Expression Omnibus: NCBI gene expression and hybridization array data repository,” Nucleic Acids Research, vol. 30, no. 1, pp. 207–210, 2002. View at Google Scholar · View at Scopus