Functional & Integrative Genomics

, Volume 5, Issue 1, pp 32–39

Empirical Bayes estimation of gene-specific effects in micro-array research

  • Jode W. Edwards
  • Grier P. Page
  • Gary Gadbury
  • Moonseong Heo
  • Tsuyoshi Kayo
  • Richard Weindruch
  • David B. Allison
Original Paper

DOI: 10.1007/s10142-004-0123-0

Cite this article as:
Edwards, J.W., Page, G.P., Gadbury, G. et al. Funct Integr Genomics (2005) 5: 32. doi:10.1007/s10142-004-0123-0

Abstract

Micro-array technology allows investigators the opportunity to measure expression levels of thousands of genes simultaneously. However, investigators are also faced with the challenge of simultaneous estimation of gene expression differences for thousands of genes with very small sample sizes. Traditional estimators of differences between treatment means (ordinary least squares estimators or OLS) are not the best estimators if interest is in estimation of gene expression differences for an ensemble of genes. In the case that gene expression differences are regarded as exchangeable samples from a common population, estimators are available that result in much smaller average mean-square error across the population of gene expression difference estimates. We have simulated the application of such an estimator, namely an empirical Bayes (EB) estimator of random effects in a hierarchical linear model (normal-normal). Simulation results revealed mean-square error as low as 0.05 times the mean-square error of OLS estimators (i.e., the difference between treatment means). We applied the analysis to an example dataset as a demonstration of the shrinkage of EB estimators and of the reduction in mean-square error, i.e., increase in precision, associated with EB estimators in this analysis. The method described here is available in software that is available at http://www.soph.uab.edu/ssg.asp?id=1087.

Keywords

Micro-arrayShrinkageEstimationEmpirical Bayes

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Jode W. Edwards
    • 1
  • Grier P. Page
    • 2
  • Gary Gadbury
    • 3
  • Moonseong Heo
    • 4
  • Tsuyoshi Kayo
    • 8
  • Richard Weindruch
    • 5
    • 6
  • David B. Allison
    • 2
    • 7
  1. 1.United States Department of Agriculture, Agricultural Research Service (USDA-ARS), Department of AgronomyIowa State UniversityAmesUSA
  2. 2.Section on Statistical GeneticsDepartment of BiostatisticsBirminghamUSA
  3. 3.Department of Mathematics and StatisticsUniversity of Missouri-RollaRolla
  4. 4.Department of Psychiatry/Westchester, Cornell Institute of Geriatric PsychiatryWeill Medical College of Cornell UniversityWhite PlainsUSA
  5. 5.Department of Medicine and the Wisconsin Primate Research CenterUniversity of WisconsinMadisonUSA
  6. 6.The Geriatric Research, Education, and Clinical CenterWilliam S. Middleton VA HospitalMadisonUSA
  7. 7.Clinical Nutrition Research CenterBirminghamUSA
  8. 8.Life Gen TechnologiesLLC c/o Mirus CorporationMadisonUSA