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A Unified Mixed Effects Model for Gene Set Analysis of Time Course Microarray Experiments


  • Wang Lily

    (Vanderbilt University)

  • Chen Xi

    (Vanderbilt University)

  • Wolfinger Russell D

    (SAS Institute Inc.)

  • Franklin Jeffrey L

    (Vanderbilt University)

  • Coffey Robert J

    (Vanderbilt University)

  • Zhang Bing

    (Vanderbilt University)


Methods for gene set analysis test for coordinated changes of a group of genes involved in the same biological process or molecular pathway. Higher statistical power is gained for gene set analysis by combining weak signals from a number of individual genes in each group. Although many gene set analysis methods have been proposed for microarray experiments with two groups, few can be applied to time course experiments. We propose a unified statistical model for analyzing time course experiments at the gene set level using random coefficient models, which fall into the more general class of mixed effects models. These models include a systematic component that models the mean trajectory for the group of genes, and a random component (the random coefficients) that models how each gene's trajectory varies about the mean trajectory.

Suggested Citation

  • Wang Lily & Chen Xi & Wolfinger Russell D & Franklin Jeffrey L & Coffey Robert J & Zhang Bing, 2009. "A Unified Mixed Effects Model for Gene Set Analysis of Time Course Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-18, November.
  • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:47

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