IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0071680.html
   My bibliography  Save this article

Identification of Bicluster Regions in a Binary Matrix and Its Applications

Author

Listed:
  • Hung-Chia Chen
  • Wen Zou
  • Yin-Jing Tien
  • James J Chen

Abstract

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of “coherence” patterns. These methods perform unsatisfactorily if the purpose is to identify biclusters of a constant level. This paper presents a two-step biclustering method to identify constant level biclusters for binary or quantitative data. This algorithm identifies the maximal dimensional submatrix such that the proportion of non-signals is less than a pre-specified tolerance δ. The proposed method has much higher sensitivity and slightly lower specificity than several prominent biclustering methods from the analysis of two synthetic datasets. It was further compared with the Bimax method for two real datasets. The proposed method was shown to perform the most robust in terms of sensitivity, number of biclusters and number of serotype-specific biclusters identified. However, dichotomization using different signal level thresholds usually leads to different sets of biclusters; this also occurs in the present analysis.

Suggested Citation

  • Hung-Chia Chen & Wen Zou & Yin-Jing Tien & James J Chen, 2013. "Identification of Bicluster Regions in a Binary Matrix and Its Applications," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-13, August.
    • Handle: RePEc:plo:pone00:0071680
    DOI: 10.1371/journal.pone.0071680
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0071680
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0071680&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0071680?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
    ---><---

    References listed on IDEAS

    as
    1. Boris Mirkin & Phipps Arabie & Lawrence Hubert, 1995. "Additive two-mode clustering: The error-variance approach revisited," Journal of Classification, Springer;The Classification Society, vol. 12(2), pages 243-263, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hung-Chia Chen & Wen Zou & Tzu-Pin Lu & James J Chen, 2014. "A Composite Model for Subgroup Identification and Prediction via Bicluster Analysis," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-14, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Willem Heiser, 2004. "Geometric representation of association between categories," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 513-545, December.
    2. Tom Wilderjans & Dirk Depril & Iven Van Mechelen, 2013. "Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 56-74, April.
    3. Michael Brusco & Patrick Doreian, 2015. "An Exact Algorithm for the Two-Mode KL-Means Partitioning Problem," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 481-515, October.
    4. Joost Rosmalen & Patrick Groenen & Javier Trejos & William Castillo, 2009. "Optimization Strategies for Two-Mode Partitioning," Journal of Classification, Springer;The Classification Society, vol. 26(2), pages 155-181, August.

    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:plo:pone00:0071680. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.