A mixture model approach to detecting differentially expressed genes with microarray data

Wei Pan, Jizhen Lin, Chap T. Le

Research output: Contribution to journalArticlepeer-review

115 Scopus citations


An exciting biological advancement over the past few years is the use of microarray technologies to measure simultaneously the expression levels of thousands of genes. The bottleneck now is how to extract useful information from the resulting large amounts of data. An important and common task in analyzing microarray data is to identify genes with altered expression under two experimental conditions. We propose a nonparametric statistical approach, called the mixture model method (MMM), to handle the problem when there are a small number of replicates under each experimental condition. Specifically, we propose estimating the distributions of a t -type test statistic and its null statistic using finite normal mixture models. A comparison of these two distributions by means of a likelihood ratio test, or simply using the tail distribution of the null statistic, can identify genes with significantly changed expression. Several methods are proposed to effectively control the false positives. The methodology is applied to a data set containing expression levels of 1,176 genes of rats with and without pneumococcal middle ear infection.

Original languageEnglish (US)
Pages (from-to)117-124
Number of pages8
JournalFunctional and Integrative Genomics
Issue number3
StatePublished - Jul 2003

Bibliographical note

Funding Information:
Acknowledgements W.P. was supported by an NIH grant (R01-HL65462) and a Minnesota Medical Foundation grant. The authors are grateful to two referees for many helpful comments and suggestions.


  • Likelihood ratio
  • Normal mixtures
  • Permutation
  • SAM


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