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Open Access

A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments

  • Qihua Tan1Email author,
  • Jesper Dahlgaard1,
  • Werner Vach2,
  • Basem M Abdallah3,
  • Moustapha Kassem3 and
  • Torben A Kruse1
Genome Biology20056:P5

Received: 7 March 2005

Published: 10 March 2005


We introduce a non-parametric approach using bootstrap-assisted correspondence analysis to identify and validate genes that are differentially expressed in factorial microarray experiments. Model comparison showed that although both parametric and non-parametric methods capture the different profiles in the data, our method is less inclined to false positive results due to dimension reduction in data analysis.


Singular Value DecompositionCorrespondence AnalysisMicroarray ExperimentInteractive VariableGene Contribution