A mixture model approach for the analysis of microarray gene expression data

David B. Allison, Gary L. Gadbury, Moonseong Heo, José R. Fernández, Cheol Koo Lee, Cheol Koo Lee, Tomas A. Prolla, Richard Weindruch, Richard Weindruch

Research output: Contribution to journalArticlepeer-review

277 Citations (Scopus)


Microarrays have emerged as powerful tools allowing investigators to assess the expression of thousands of genes in different tissues and organisms. Statistical treatment of the resulting data remains a substantial challenge. Investigators using microarray expression studies may wish to answer questions about the statistical significance of differences in expression of any of the genes under study, avoiding false positive and false negative results. We have developed a sequence of procedures involving finite mixture modeling and bootstrap inference to address these issues in studies involving many thousands of genes. We illustrate the use of these techniques with a dataset involving calorically restricted mice.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalComputational Statistics and Data Analysis
Issue number5
Publication statusPublished - 2002 Mar 28
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported in part by NIH grants R29DK47256, R01DK51716, P30DK26687, P01AG11915, R01ES09912 and 5 U24 DK58776. We are grateful to Drs. Nicholas Schork, Joel Horowitz, and Michael C. Neale for their helpful comments.

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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