期刊
ANNALS OF APPLIED STATISTICS
卷 5, 期 2A, 页码 725-745出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/10-AOAS425
关键词
Bayesian network; dependency network; Gaussian graphical model; hierarchical model; interventional data; Markov chain Monte Carlo; mixture distribution; single cell measurements; signaling pathway
资金
- National Science Foundation [DMS-07-14817]
- National Heart, Lung, and Blood Institute [N01 HV28186]
- National Institute of Drug Abuse [P30 DA018343]
- National Institute of General Medical Sciences [R01 GM59507]
- Yale University Biomedical High Performance Computing Center
- NIH [RR19895]
Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. With such data collected under different stimulatory or inhibitory conditions, it is possible to infer the causal relationships among proteins from single cell interventional data. In this article we propose a Bayesian hierarchical modeling framework to infer the signaling pathway based on the posterior distributions of parameters in the model. Under this framework, we consider network sparsity and model the existence of an association between two proteins both at the overall level across all experiments and at each individual experimental level. This allows us to infer the pairs of proteins that are associated with each other and their causal relationships. We also explicitly consider both intrinsic noise and measurement error. Markov chain Monte Carlo is implemented for statistical inference. We demonstrate that this hierarchical modeling can effectively pool information from different interventional experiments through simulation studies and real data analysis.
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