A Bayesian hierarchical model for inference across related reverse phase protein arrays experiments

Riten Mitra, Peter Müller, Yuan Ji, Yitan Zhu, Gordon Mills, Yiling Lu

    Research output: Contribution to journalArticle

    Abstract

    We consider inference for functional proteomics experiments that record protein activation over time following perturbation under different dose levels of several drugs. The main inference goal is the dependence structure of the selected proteins. A critical challenge is the lack of sufficient data under any one drug and dose level to allow meaningful inference on dependence structure. We propose a hierarchical model to implement the desired inference. The key element of the model is a shared dependence structure on (latent) binary indicators of protein activation.

    Original languageEnglish (US)
    Pages (from-to)2483-2492
    Number of pages10
    JournalJournal of Applied Statistics
    Volume41
    Issue number11
    DOIs
    StatePublished - Nov 2 2014

    Keywords

    • Bayesian
    • graphical
    • hierarchical model
    • protein networks
    • timecourse

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty

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