A Method for Normalizing Microarrays Using Genes that are Not Differentially Expressed

Cavan Reilly, Changchun Wang, Mark Rutherford

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

One of the more challenging, yet easily overlooked, aspects of the analysis of microarrays is how to normalize arrays so that comparisons can be made across arrays. Most studies that utilize microarrays to detect differential gene expression between samples find the data only enable one to conclude that a handful of genes are differentially expressed. The basic idea here is to use the genes that are not differentially expressed to conduct the normalization. Of course, because one cannot determine which genes are differentially expressed until the normalization is conducted, this is a nontrivial problem. Here a general framework and computational method (using the Gibbs sampler) is devised to allow for such normalization. We apply the method to a gene expression experiment aimed at furthering our understanding of Porcine reproductive and respiratory syndrome virus, a major source of economic loss in the swine industry.

Original languageEnglish (US)
Pages (from-to)868-878
Number of pages11
JournalJournal of the American Statistical Association
Volume98
Issue number464
DOIs
StatePublished - Dec 1 2003

Keywords

  • Bayesian inference
  • Gene expression
  • Gibbs sampler
  • M-A plots
  • Microarray normalization
  • Porcine reproductive and respiratory syndrome virus

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