IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v4y2005i1n10.html
   My bibliography  Save this article

Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors

Author

Listed:
  • Feten Guri

    (Norwegian University of Life Sciences)

  • Almøy Trygve

    (Norwegian University of Life Sciences)

  • Aastveit Are H.

    (Norwegian University of Life Sciences)

Abstract

Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.

Suggested Citation

  • Feten Guri & Almøy Trygve & Aastveit Are H., 2005. "Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-18, May.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:10
    DOI: 10.2202/1544-6115.1120
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1120
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1120?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. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
    2. Faisal Shahla & Tutz Gerhard, 2017. "Missing value imputation for gene expression data by tailored nearest neighbors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 95-106, April.

    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:4:y:2005:i:1:n:10. 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.