IDEAS home Printed from https://ideas.repec.org/p/bep/jhubio/1053.html
   My bibliography  Save this paper

MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data

Author

Listed:
  • Leslie Cope

    (Departments of Oncology and Biostatistics, Johns Hopkins University)

  • Xiaogang Zhong

    (Department of Applied Mathematics, Johns Hopkins University)

  • Elizabeth Garrett-Mayer

    (Departments of Oncology and Biostatistics, Johns Hopkins University)

  • Giovanni Parmigiani

    (The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health)

Abstract

Cross-study validation of gene expression investigations is critical in genomic analysis. We developed an R package and associated object definitions to merge and visualize multiple gene expression datasets. Our merging functions use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include "integrative correlation" plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression. Availability: Free open source from url http://www.bioconductor.org. Contact: Xiaogang Zhong zhong@ams.jhu.edu Supplementary information: Documentation available with the package.

Suggested Citation

  • Leslie Cope & Xiaogang Zhong & Elizabeth Garrett-Mayer & Giovanni Parmigiani, 2004. "MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1053, Berkeley Electronic Press.
  • Handle: RePEc:bep:jhubio:1053
    Note: oai:bepress.com:jhubiostat-1053
    as

    Download full text from publisher

    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1053&context=jhubiostat
    Download Restriction: no
    ---><---

    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:bep:jhubio:1053. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.bepress.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.