Annals of Epidemiology
Volume 20, Issue 10 , Pages 766-771 , October 2010

Detecting Differentially Expressed Genes: Minimizing Burden of Testing and Maximizing Number of Discoveries

  • Wen-Chung Lee, MD, PhD

      Affiliations

    • Corresponding Author InformationAddress correspondence to: Wen-Chung Lee, MD, PhD, Rm. 536, No. 17, Xuzhou Rd., Taipei 100, Taiwan. Fax: 886-2-23511955.

Received 6 April 2009 ,Accepted 5 April 2010.

References 

  1. Lockhart DJ, Winzeler EA. Genomics, gene expression and DNA arrays. Nature. 2000;405:827–836
  2. Duyk GM. Sharper tools and simpler methods. Nat Genet. 2002;32:465–468
  3. Dalma-Weiszhausz DD, Chicurel ME, Gingeras TR. Microarrays and genetic epidemiology: a multipurpose tool for a multifaceted field. Genet Epidemiol. 2002;23:4–20
  4. Simon R, Radmacher MD, Dobbin K. Design of studies using DNA microarrays. Genet Epidemiol. 2002;23:21–36
  5. Satagopan JM, Panageas KS. A statistical perspective on gene expression data analysis. Stat Med. 2003;22:481–499
  6. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100:9440–9445
  7. Manly KF, Nettleton D, Hwang JTG. Genomics, prior probability, and statistical tests of multiple hypotheses. Genome Res. 2004;14:997–1001
  8. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc (B). 1995;57:289–300
  9. Storey J. A direct approach to false discovery rates. J Roy Stat Soc (B). 2002;64:479–498
  10. Storey J. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat. 2003;31:2013–2035
  11. Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507
  12. Wakefield JA. Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet. 2007;81:208–227
  13. Lee MLT, Whitmore GA. Power and sample size for DNA microarray studies. Stat Med. 2002;21:3543–3570
  14. Jung SH, Bang H, Young S. Sample size calculation for multiple testing in microarray data analysis. Biostatistics. 2005;6:157–169
  15. Li SS, Bigler J, Lampe JW, Potter JD, Feng Z. FDR-controlling testing procedures and sample size determination for microarrays. Stat Med. 2005;24:2267–2280
  16. Pavlidis P, Li Q, Noble WS. The effect of replication on gene expression microarray experiments. Bioinformatics. 2003;19:1620–1627
  17. Rosner B. Fundamentals of Biostatistics. 3rd ed.. Boston, Massachusetts: PWS-KENT Publishing; 1990;
  18. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881–885
  19. The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–678
  20. Khoury MJ, Wacholder S. Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies—challenges and opportunities. Am J Epidemiol. 2008;169:227–230

PII: S1047-2797(10)00082-7

doi: 10.1016/j.annepidem.2010.04.002

Annals of Epidemiology
Volume 20, Issue 10 , Pages 766-771 , October 2010