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, Tomas A. Prolla, Richard Weindruch

Research output: Contribution to journalArticle

260 Citations (Scopus)

Abstract

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 (US)
Pages (from-to)1-20
Number of pages20
JournalComputational Statistics and Data Analysis
Volume38
Issue number5
StatePublished - Mar 28 2002
Externally publishedYes

Fingerprint

Microarrays
Gene Expression Data
Microarray Data
Mixture Model
Gene expression
Genes
Gene
Microarray
Mixture Modeling
Finite Mixture
Statistical Significance
False Positive
Bootstrap
Mouse
Tissue
Mixture model

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Statistics, Probability and Uncertainty
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Numerical Analysis
  • Statistics and Probability

Cite this

Allison, D. B., Gadbury, G. L., Heo, M., Fernández, J. R., Lee, C. K., Prolla, T. A., & Weindruch, R. (2002). A mixture model approach for the analysis of microarray gene expression data. Computational Statistics and Data Analysis, 38(5), 1-20.

A mixture model approach for the analysis of microarray gene expression data. / Allison, David B.; Gadbury, Gary L.; Heo, Moonseong; Fernández, José R.; Lee, Cheol Koo; Prolla, Tomas A.; Weindruch, Richard.

In: Computational Statistics and Data Analysis, Vol. 38, No. 5, 28.03.2002, p. 1-20.

Research output: Contribution to journalArticle

Allison, DB, Gadbury, GL, Heo, M, Fernández, JR, Lee, CK, Prolla, TA & Weindruch, R 2002, 'A mixture model approach for the analysis of microarray gene expression data', Computational Statistics and Data Analysis, vol. 38, no. 5, pp. 1-20.
Allison DB, Gadbury GL, Heo M, Fernández JR, Lee CK, Prolla TA et al. A mixture model approach for the analysis of microarray gene expression data. Computational Statistics and Data Analysis. 2002 Mar 28;38(5):1-20.
Allison, David B. ; Gadbury, Gary L. ; Heo, Moonseong ; Fernández, José R. ; Lee, Cheol Koo ; Prolla, Tomas A. ; Weindruch, Richard. / A mixture model approach for the analysis of microarray gene expression data. In: Computational Statistics and Data Analysis. 2002 ; Vol. 38, No. 5. pp. 1-20.
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