title: A Bayesian hierarchical model for inference across related reverse phase protein arrays experiments.
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.
authors.affiliation: ICES, University of Texas at Austin, Austin, TX, USA.
authors.forename: Riten
authors.initials: R
authors.lastname: Mitra
authors.affiliation: Department of Mathematics, University of Texas at Austin, Austin, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.affiliation: CCRI, Northshore University HealthSystem, Chicago, IL, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.affiliation: CCRI, Northshore University HealthSystem, Chicago, IL, USA.
authors.forename: Yitan
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Systems Biology, UT MD Anderson Cancer Centre, Houston, TX, USA.
authors.forename: Gordon
authors.initials: G
authors.lastname: Mills
authors.affiliation: Department of Systems Biology, UT MD Anderson Cancer Centre, Houston, TX, USA.
authors.forename: Yiling
authors.initials: Y
authors.lastname: Lu
keywords: Bayesian; graphical; hierarchical model; protein networks; timecourse