IDEAS home Printed from
   My bibliography  Save this article

A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers


  • Lucas Joseph

    (Duke Institute for Genome Sciences and Policy)

  • Carvalho Carlos

    (University of Chicago Graduate School of Business)

  • West Mike

    (Duke Department of Statistical Science)


We describe a strategy for the analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing downstream biological pathway events following interventions in designed experiments. When translated into in vivo human observational data, analysis using sparse latent factor models can yield multiple quantitative factors characterizing expression patterns that are often more complex than in the controlled, in vitro setting. The estimation of common patterns in expression that reflect all aspects of covariation evident in vivo offers an enhanced, modular view of the complexity of biological associations of signature genes. This can identify substructure in the biological process under experimental investigation and improved biomarkers of clinical outcomes. We illustrate the approach in a detailed study from an oncogene intervention experiment where in vivo factor profiling of an in vitro signature generates biological insights related to underlying pathway activities and chromosomal structure, and leads to refinements of cancer recurrence risk stratification across several cancer studies.

Suggested Citation

  • Lucas Joseph & Carvalho Carlos & West Mike, 2009. "A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, February.
  • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:11

    Download full text from publisher

    File URL:
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Carvalho, Carlos M. & Chang, Jeffrey & Lucas, Joseph E. & Nevins, Joseph R. & Wang, Quanli & West, Mike, 2008. "High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1438-1456.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Zheng Lingling & Yan Xiao & Suchindran Sunil & Dressman Holly & Chute John P. & Lucas Joseph, 2014. "Biological pathway selection through Bayesian integrative modeling," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(6), pages 1-1, December.
    2. Bonassi Fernando V. & You Lingchong & West Mike, 2011. "Bayesian Learning from Marginal Data in Bionetwork Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-27, October.

    More about this item


    Access and download statistics


    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:8:y:2009:i:1:n:11. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.