IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v4y2008i1n3.html
   My bibliography  Save this article

Biclustering of Gene Expression Data by an Extension of Mixtures of Factor Analyzers

Author

Listed:
  • Martella Francesca

    (Sapienza, Università di Roma)

  • Alfò Marco

    (Sapienza, Università di Roma)

  • Vichi Maurizio

    (Sapienza, Università di Roma)

Abstract

A challenge in microarray data analysis concerns discovering local structures composed by sets of genes that show homogeneous expression patterns across subsets of conditions. We present an extension of the mixture of factor analyzers model (MFA) allowing for simultaneous clustering of genes and conditions. The proposed model is rather flexible since it models the density of high-dimensional data assuming a mixture of Gaussian distributions with a particular omponent-specific covariance structure. Specifically, a binary and row stochastic matrix representing tissue membership is used to cluster tissues (experimental conditions), whereas the traditional mixture approach is used to define the gene clustering. An alternating expectation conditional maximization (AECM) algorithm is proposed for parameter estimation; experiments on simulated and real data show the efficiency of our method as a general approach to biclustering. The Matlab code of the algorithm is available upon request from authors.

Suggested Citation

  • Martella Francesca & Alfò Marco & Vichi Maurizio, 2008. "Biclustering of Gene Expression Data by an Extension of Mixtures of Factor Analyzers," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-21, February.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:3
    DOI: 10.2202/1557-4679.1078
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1078
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1078?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. Ferraro, Maria Brigida & Giordani, Paolo & Vichi, Maurizio, 2021. "A class of two-mode clustering algorithms in a fuzzy setting," Econometrics and Statistics, Elsevier, vol. 18(C), pages 63-78.
    2. Vicari, Donatella & Alfó, Marco, 2014. "Model based clustering of customer choice data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 3-13.

    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:ijbist:v:4:y:2008:i:1:n:3. 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.