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

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

Journal of Applied Statistics, 2014, vol. 41, issue 11, 2483-2492

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.

Date: 2014
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DOI: 10.1080/02664763.2014.920776

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