American Statistical Association New York City Metropolitan Area Chapter Mailman School of Public Health Columbia University Department of Biostatistics Colloquium

Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. In contrast to measurements based on aggregated cells, e.g. gene expression analysis from microarrays, single cell-based measures provide much richer information on the cell states and signaling networks. In this presentation, we discuss a hierarchical model for signaling network reconstruction based on single cell measurements. This modeling framework can effectively pool information from different perturbation experiments and the network sparsity is also explicitly modeled. We will describe the Monte Carlo Markov Chain method for model inference. Simulation results demonstrate the superiority of the hierarchical approach. The usefulness of our model will also be illustrated through its application to the intracellular signaling networks of human primary naive CD4+ T cells, downstream of CD3, CD28, and LFA-1 activation.