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A Significance Method for Time Course Microarray Experiments Applied to Two Human Studies

Author

Listed:
  • John Storey

    (University of Washington)

  • Jeffrey Leek

    (University of Washington)

  • Wenzhong Xiao

    (Stanford University)

  • James Dai

    (University of Washington)

  • Ron Davis

    (Stanford University)

Abstract

A common goal in microarray experiments is to identify genes that are differentially expressed among two or more biological conditions. There is currently no standard methodology for detecting differential expression in time course studies. However, it is clear that monitoring the behavior of gene expression over time is important and will be a common experimental design in the future. Here we present a general statistical significance method for detecting temporal differential expression that can be applied to the typical types of comparisons and sampling schemes. We apply this method to two studies that we have carried out on humans. The goal of one study is to identify genes showing temporal differential expression between controls and endotoxin-treated individuals, and the other is to identify genes that show aging effects in the kidney. Genes identified in both studies corroborate previous findings and also provide novel insights. This methodology has been implemented in the freely distributed EDGE software package.

Suggested Citation

  • John Storey & Jeffrey Leek & Wenzhong Xiao & James Dai & Ron Davis, 2004. "A Significance Method for Time Course Microarray Experiments Applied to Two Human Studies," UW Biostatistics Working Paper Series 1065, Berkeley Electronic Press.
  • Handle: RePEc:bep:uwabio:1065
    Note: oai:bepress.com:uwbiostat-1065
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    Citations

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    Cited by:

    1. Alessio Farcomeni, 2007. "Some Results on the Control of the False Discovery Rate under Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 275-297, June.
    2. Yuan, Ming, 2006. "Flexible temporal expression profile modelling using the Gaussian process," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1754-1764, December.

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