Causal Discovery from Mass Cytometry Data: Karen Sachs
Abstract: Single cell data provides a rich source of data for statistical inference among biological variables of interest, including proteins and mRNA species. We previously demonstrated the application of Bayesian networks to structure learning of the underlying joint distribution among the variables, and showed that causal connections can be inferred. However, many open questions remain regarding the use of this approach in molecular biology. In this talk, we will discuss confounding factors and approaches that we have developed to circumvent them. We will also discuss opportunities for practical application in molecular biology with a focus on cancer, and show examples of applications.