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The Cyclohedron Test for Finding Periodic Genes in Time Course Expression Studies

Author

Listed:
  • Morton Jason

    (University of California, Berkeley)

  • Pachter Lior

    (University of California, Berkeley)

  • Shiu Anne

    (University of California, Berkeley)

  • Sturmfels Bernd

    (University of California, Berkeley)

Abstract

The problem of finding periodically expressed genes from time course microarray experiments is at the center of numerous efforts to identify the molecular components of biological clocks. We present a new approach to this problem based on the cyclohedron test, which is a rank test inspired by recent advances in algebraic combinatorics. The test has the advantage of being robust to measurement errors, and can be used to ascertain the significance of top-ranked genes. We apply the test to recently published measurements of gene expression during mouse somitogenesis and find 32 genes that collectively are significant. Among these are previously identified periodic genes involved in the Notch/FGF and Wnt signaling pathways, as well as novel candidate genes that may play a role in regulating the segmentation clock. These results confirm that there are an abundance of exceptionally periodic genes expressed during somitogenesis. The emphasis of this paper is on the statistics and combinatorics that underlie the cyclohedron test and its implementation within a multiple testing framework.

Suggested Citation

  • Morton Jason & Pachter Lior & Shiu Anne & Sturmfels Bernd, 2007. "The Cyclohedron Test for Finding Periodic Genes in Time Course Expression Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-25, August.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:21
    DOI: 10.2202/1544-6115.1286
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    Cited by:

    1. Alan L Hutchison & Mark Maienschein-Cline & Andrew H Chiang & S M Ali Tabei & Herman Gudjonson & Neil Bahroos & Ravi Allada & Aaron R Dinner, 2015. "Improved Statistical Methods Enable Greater Sensitivity in Rhythm Detection for Genome-Wide Data," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-29, March.

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