Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data

TL;DR

This paper introduces a Bayesian hierarchical model to infer protein signaling pathways from single-cell interventional data, accounting for network sparsity, noise, and measurement error, and pooling information across experiments.

Contribution

It presents a novel Bayesian hierarchical framework for pathway inference that incorporates sparsity, noise, and inter-experiment information sharing.

Findings

Effective pathway inference demonstrated through simulations

Successful application to real single-cell data

Model accounts for measurement error and intrinsic noise

Abstract

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.