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Gene classification for microarray data with multiple time measurements

Background

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.

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Correspondence to Jonathan Quiton.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Quiton, J., Rinehart, C., Chavarria-Smith, J. et al. Gene classification for microarray data with multiple time measurements. BMC Bioinformatics 9, P18 (2008). https://doi.org/10.1186/1471-2105-9-S7-P18

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Keywords

  • Gene Expression
  • Data Analysis
  • Treatment Group
  • Treatment Response
  • Microarray Data