Analyzing factorial designed microarray experiments

Denise Scholtens*, Alexander Miron, Faisal M. Merchant, Arden Miller, Penelope L. Miron, J. Dirk Iglehart, Robert Gentleman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


High-throughput quantification of gene expression using microarray technology has dramatically changed biological investigation into the roles of genes in normal cell functioning, as well as the mechanisms of disease. We discuss an analytic approach for framing biological questions in terms of statistical parameters to efficiently and confidently answer questions of interest using microarray data from factorial designed experiments. Investigators can extract pertinent and interpretable information from the data about the effects of the factors, their interactions with each other, and the statistical significance of these effects, rather than rely solely on clustering techniques or fold change point estimates in hopes of finding co-expressed genes. By first examining how biological mechanisms are reflected in mRNA transcript abundance, investigators can better design microarray experiments to answer the most interesting questions.

Original languageEnglish (US)
Pages (from-to)19-43
Number of pages25
JournalJournal of Multivariate Analysis
Issue number1 SPEC. ISS.
StatePublished - Jul 2004


  • Estrogen target
  • Experimental design
  • Gene expression
  • Gene selection
  • Linear model

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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