Table of Contents Author Guidelines Submit a Manuscript
Scientifica
Volume 2012 (2012), Article ID 519394, 9 pages
http://dx.doi.org/10.6064/2012/519394
Research Article

A Comparison of Two Classes of Methods for Estimating False Discovery Rates in Microarray Studies

1Cancer Research and Biostatistics, Seattle, WA 98101, USA
2Department of Biostatistics, University of Washington, Seattle, WA 98195, USA

Received 9 May 2012; Accepted 27 May 2012

Academic Editors: M. Araki, P. Carbonell, J. A. Castro, and K. Jung

Copyright © 2012 Emily Hansen and Kathleen F. Kerr. 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. Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society Series B, vol. 57, pp. 289–300, 1995. View at Google Scholar
  2. D. B. Allison, X. Cui, G. P. Page, and M. Sabripour, “Microarray data analysis: from disarray to consolidation and consensus,” Nature Reviews Genetics, vol. 7, no. 1, pp. 55–65, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Dupuy and R. M. Simon, “Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting,” Journal of the National Cancer Institute, vol. 99, no. 2, pp. 147–157, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. 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
  5. P. Broberg, “Statistical methods for ranking differentially expressed genes,” Genome Biology, vol. 4, no. 6, article R41, 2003. View at Google Scholar · View at Scopus
  6. Y. Xie, W. Pan, and A. B. Khodursky, “A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data,” Bioinformatics, vol. 21, no. 23, pp. 4280–4288, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Efron, R. Tibshirani, J. D. Storey, and V. Tusher, “Empirical Bayes Analysis of a Microarray Experiment,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1151–1160, 2001. View at Google Scholar · View at Scopus
  8. P. Baldi and A. D. Long, “A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes,” Bioinformatics, vol. 17, no. 6, pp. 509–519, 2001. View at Google Scholar · View at Scopus
  9. I. Lönnstedt and T. Speed, “Replicated microarray data,” Statistica Sinica, vol. 12, no. 1, pp. 31–46, 2002. View at Google Scholar · View at Scopus
  10. G. K. Smyth, “Linear models and empirical bayes methods for assessing differential expression in microarray experiments,” Statistical Applications in Genetics and Molecular Biology, vol. 3, no. 1, article 3, 2004. View at Google Scholar · View at Scopus
  11. X. Cui, J. T. Hwang, J. Qiu, N. J. Blades, and G. A. Churchill, “Improved statistical tests for differential gene expression by shrinking variance components estimates,” Biostatistics (Oxford, England), vol. 6, no. 1, pp. 59–75, 2005. View at Google Scholar · View at Scopus
  12. C. M. Kendziorski, M. A. Newton, H. Lan, and M. N. Gould, “On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles,” Statistics in Medicine, vol. 22, no. 24, pp. 3899–3914, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. G. W. Wright and R. M. Simon, “A random variance model for detection of differential gene expression in small microarray experiments,” Bioinformatics, vol. 19, no. 18, pp. 2448–2455, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. L.-X. Qin, K. F. Kerr, A. Boyles et al., “Empirical evaluation of data transformations and ranking statistics for microarray analysis,” Nucleic Acids Research, vol. 32, no. 18, pp. 5471–5479, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Zhang and J. Cao, “A close examination of double filtering with fold change and t test in microarray analysis,” BMC Bioinformatics, vol. 10, no. 1, article 402, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. D. B. Allison, G. L. Gadbury, M. Heo et al., “A mixture model approach for the analysis of microarray gene expression data,” Computational Statistics and Data Analysis, vol. 39, no. 1, pp. 1–20, 2002. View at Google Scholar · View at Scopus
  17. K. F. Kerr, “Comments on the analysis of unbalanced microarray data,” Bioinformatics, vol. 25, no. 16, pp. 2035–2041, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Efron, “Size, power and false discovery rates,” Annals of Statistics, vol. 35, no. 4, pp. 1351–1377, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Strimmer, “A unified approach to false discovery rate estimation,” BMC Bioinformatics, vol. 9, article 303, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Strimmer, “fdrtool: a versatile R package for estimating local and tail area-based false discovery rates,” Bioinformatics, vol. 24, no. 12, pp. 1461–1462, 2008. View at Publisher · View at Google Scholar · View at Scopus
  21. J. D. Storey and R. Tibshirani, “Statistical significance for genomewide studies,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 16, pp. 9440–9445, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Langaas, B. H. Lindqvist, and E. Ferkingstad, “Estimating the proportion of true null hypotheses, with application to DNA microarray data,” Journal of the Royal Statistical Society Series B, vol. 67, no. 4, pp. 555–572, 2005. View at Publisher · View at Google Scholar · View at Scopus