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Open Access

Gene classification for microarray data with multiple time measurements

  • Jonathan Quiton1Email author,
  • Claire Rinehart2,
  • Joseph Chavarria-Smith2 and
  • Nancy Rice2
BMC Bioinformatics20089(Suppl 7):P18

Published: 8 July 2008


Gene ExpressionData AnalysisTreatment GroupTreatment ResponseMicroarray Data


In microarray data analysis, we considered the problem of classifying genes based on ratio of the mean gene expression levels between the control and the treatment factors measured at t fixed times. In this setting, we assume that the control and treatment responses come from two independent normal populations, and the two treatment groups are significantly different only if the ratio of the two population means is less than r1 or greater than r2. We propose an approach based on the mapping of the T scores into C = {+1, 0, -1}, where +1 is the value when the t-score is greater than the upper critical point, -1 if it is less than the lower critical point, and 0 otherwise. Misclassification probability under small replications is given and the method is demonstrated using a microarray data.

Authors’ Affiliations

Department of Mathematics, Western Kentucky University, Bowling Green, USA
Department of Biology, Western Kentucky University, Bowling Green, USA


© Quiton et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd.