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Significance Analysis of Time-Course Gene Expression Profiles

  • Fang-Xiang Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4463)

Abstract

This paper proposes a statistical method for significance analysis of time-course gene expression profiles, called SATgene. The SATgene models time-dependent gene expression profiles by autoregressive equations plus Gaussian noises, and time-independent gene expression profiles by constant numbers plus Gaussian noises. The statistical F-testing for regression analysis is used to calculate the confidence probability (significance level) that a time-course gene expression profile is not time-independent. The user can use this confidence probability to select significantly expressed genes from a time-course gene expression dataset. Both one synthetic dataset and one biological dataset were employed to evaluate the performance of the SATgene, compared to traditional gene selection methods: the pairwise R-fold change method and the standard deviation method. The results show that the SATgene outperforms the traditional methods.

Keywords

time-course gene expression time-dependence autoregressive model F-testing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fang-Xiang Wu
    • 1
  1. 1.Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9Canada

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