Bayesian hierarchical models for protein networks in single-cell mass cytometry.

We propose a class of hierarchical models to investigate the protein functional network of cellular markers. We consider a novel data set from single-cell proteomics. The data are generated from single-cell mass cytometry experiments, in which protein expression is measured within an individual cell for multiple markers. Tens of thousands of cells are measured serving as biological replicates. Applying the Bayesian models, we report protein functional networks under different experimental conditions and the differences between the networks, ie, differential networks. We also present the differential network in a novel fashion that allows direct observation of the links between the experimental agent and its putative targeted proteins based on posterior inference. Our method serves as a powerful tool for studying molecular interactions at cellular level.