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


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