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

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...

Full description

Saved in:
Bibliographic Details
Published inJournal of applied statistics Vol. 41; no. 11; pp. 2483 - 2492
Main Authors Mitra, Riten, Müller, Peter, Ji, Yuan, Zhu, Yitan, Mills, Gordon, Lu, Yiling
Format Journal Article
LanguageEnglish
Published England Taylor & Francis 02.11.2014
Taylor & Francis Ltd
Subjects
Activation
Applied statistics
Arrays
Bayesian
Bayesian analysis
Drug dosages
Drugs
graphical
hierarchical model
Inference
Mathematical models
Perturbation methods
Pharmacology
protein networks
Proteins
Proteomics
Studies
timecourse
timecourse
hierarchical model
protein networks
Bayesian
graphical
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2014.920776