IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-34675-1_3.html
   My bibliography  Save this book chapter

A Study of the Correlation Structure of Microarray Gene Expression Data Based on Mechanistic Modeling of Cell Population Kinetics

In: Statistical Modeling for Biological Systems

Author

Listed:
  • Linlin Chen

    (Rochester Institute of Technology, School of Mathematical Sciences)

  • Lev Klebanov

    (Charles University, Department of Probability and Statistics)

  • Anthony Almudevar

    (University of Rochester, Department of Biostatistics and Computational Biology)

  • Christoph Proschel

    (University of Rochester, Department of Biomedical Genetics)

  • Andrei Yakovlev

    (University of Rochester, Department of Biostatistics and Computational Biology)

Abstract

Sample correlations between gene pairs within expression profiles are potentially informative regarding gene regulatory pathway structure. However, as is the case with other statistical summaries, observed correlation may be induced or suppressed by factors which are unrelated to gene functionality. In this paper, we consider the effect of heterogeneity on observed correlations, both at the tissue and subject level. Using gene expression profiles from highly enriched samples of three distinct embryonic glial cell types of the rodent neural tube, the effect of tissue heterogeneity on correlations is directly estimated for a simple two component model. Then, a stochastic model of cell population kinetics is used to assess correlation effects for more complex mixtures. Finally, a mathematical model for correlation effects of subject-level heterogeneity is developed. Although decomposition of correlation into functional and nonfunctional sources will generally not be possible, since this depends on nonobservable parameters, reasonable bounds on the size of such effects can be made using the methods proposed here.

Suggested Citation

  • Linlin Chen & Lev Klebanov & Anthony Almudevar & Christoph Proschel & Andrei Yakovlev, 2020. "A Study of the Correlation Structure of Microarray Gene Expression Data Based on Mechanistic Modeling of Cell Population Kinetics," Springer Books, in: Anthony Almudevar & David Oakes & Jack Hall (ed.), Statistical Modeling for Biological Systems, pages 47-61, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-34675-1_3
    DOI: 10.1007/978-3-030-34675-1_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;

    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:spr:sprchp:978-3-030-34675-1_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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.