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An Order Estimation Based Approach to Identify Response Genes for Microarray Time Course Data

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  • Lu Zhiheng K.

    (Metastract Inc.)

  • O. Brian Allen

    (University of Guelph)

  • Desmond Anthony F.

    (University of Guelph)

Abstract

Gene expression profiles from microarray time course experiments provide a unique opportunity to examine genome-wide signal processing and gene responses. A fundamental issue in microarray experiments is that the treatment condition can only be controlled at the cell level rather than at the gene level. The treatment condition does not affect all genes equally. Some genes depend on other genes to detect external changes. The dependency between genes is not fully deterministic and may vary with treatment condition. Thus the expression of each gene is potentially affected by two confounding effects: the treatment effect and the gene context effect arising from the regulatory interactions among genes. This gene context effect is hard to isolate. Neither can it be simply ignored. Instead, this gene context information which may be different under different treatment conditions is of primary biological interest.

Suggested Citation

  • Lu Zhiheng K. & O. Brian Allen & Desmond Anthony F., 2012. "An Order Estimation Based Approach to Identify Response Genes for Microarray Time Course Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(6), pages 1-34, December.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:6:p:1-34:n:4
    DOI: 10.1515/1544-6115.1818
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    References listed on IDEAS

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    1. Yuan, Ming & Kendziorski, Christina, 2006. "Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1323-1332, December.
    2. Hanfeng Chen & Jiahua Chen & John D. Kalbfleisch, 2001. "A modified likelihood ratio test for homogeneity in finite mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 19-29.
    3. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
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