Sparse Bayesian graphical models for RPPA time course data

Riten Mitra, Peter Mueller, Yuan Ji, Gordon Mills, Yiling Lu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    Abstract

    Advances in functional proteomic technologies have significantly enriched our knowledge of protein functions and their interactions in bio-molecular pathways. We discuss inference for RPPA (reverse phase protein array) data that measure the expression of the protein markers over time. We exploit the dynamical nature of the experiment to build a directed network of protein interactions. For this, we employ a Bayesian graphical model with an informative prior that favors sparsity. Conditional on the network, we model dependence at the level of latent binary indicators rather than the raw expression measurements. One of the key features of the proposed approach is a hierarchical model that allows for the dependence structure to be shared across different experiments, in the case of the motivating application across different drugs and doses. This is critical to facilitate meaningful inference with the limited available sample sizes. The second key feature is a sparsity inducing prior on the dependence structure.

    Original languageEnglish (US)
    Title of host publicationProceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
    Pages113-117
    Number of pages5
    DOIs
    StatePublished - 2012
    Event2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012 - Washington, DC, United States
    Duration: Dec 2 2012Dec 4 2012

    Publication series

    NameProceedings - IEEE International Workshop on Genomic Signal Processing and Statistics
    ISSN (Print)2150-3001
    ISSN (Electronic)2150-301X

    Other

    Other2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012
    CountryUnited States
    CityWashington, DC
    Period12/2/1212/4/12

    ASJC Scopus subject areas

    • Biochemistry, Genetics and Molecular Biology (miscellaneous)
    • Computational Theory and Mathematics
    • Signal Processing
    • Biomedical Engineering

    Fingerprint Dive into the research topics of 'Sparse Bayesian graphical models for RPPA time course data'. Together they form a unique fingerprint.

    Cite this