IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v12y2013i2p143-174n1001.html
   My bibliography  Save this article

Modeling the DNA copy number aberration patterns in observational high-throughput cancer data

Author

Listed:
  • van Wieringen Wessel N.

    (Department of Epidemiology and Biostatistics, VU University Medical Center, P.O. Box 7057, MB 1007, Amsterdam, The Netherlands Department of Mathematics, VU University Amsterdam, De Boelelaan 1081a, HV 1081, Amsterdam, The Netherlands)

  • Roś Beata P.

    (Department of Mathematics, VU University Amsterdam, De Boelelaan 1081a, HV 1081, Amsterdam, The Netherlands)

  • Wilting Saskia M.

    (Department of Pathology, VU University Medical Center, P.O. Box 7057, MB 1007, Amsterdam, The Netherlands)

Abstract

The process of occurrence of genomic aberrations over time in the genetic material of cancer cells reflects the progression of the cancer. Modern technologies like aCGH (array Comparative Genomic Hybridization) and MPS (Massive Parallel Sequencing) provide high-resolution measurements of DNA copy number aberrations, that reveal the full scale of genomic aberrations. A continuous time Markov chain model is proposed to describe the accumulation of aberrations over time. Time however is a latent variable (with the number of aberrations as a proxy). Integrating out time, yields the distribution of the observed DNA copy number data. The model parameters are estimated from high-dimensional DNA copy number data by means of penalized maximum pseudo- and likelihood and method of moments procedures. Having fitted the model, posterior time estimates of the advancement of each sample’s cancer are obtained and the most likely locations of a sample’s aberrations are predicted. The three estimation methods are compared in a simulation study. The paper closes with an application of the proposed methodology on cancer data.

Suggested Citation

  • van Wieringen Wessel N. & Roś Beata P. & Wilting Saskia M., 2013. "Modeling the DNA copy number aberration patterns in observational high-throughput cancer data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(2), pages 143-174.
    • Handle: RePEc:bpj:sagmbi:v:12:y:2013:i:2:p:143-174:n:1001
    DOI: 10.1515/sagmb-2012-0020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2012-0020
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2012-0020?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.

    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:12:y:2013:i:2:p:143-174:n:1001. 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.