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

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Abstract

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.

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Correspondence to Qihua Tan.

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Tan, Q., Dahlgaard, J., Vach, W. et al. A non-parametric approach for identifying differentially expressed genes in factorial microarray experiments. Genome Biol 6, P5 (2005) doi:10.1186/gb-2005-6-4-p5

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Keywords

  • Singular Value Decomposition
  • Correspondence Analysis
  • Microarray Experiment
  • Interactive Variable
  • Gene Contribution