IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v6y2007i1n21.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1286
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1286?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:21. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.