Single-cell transcriptomic data has the potential to radically redefine our view of cell type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods that automatically identify cell types and their properties based on expression profiles can be used to uncover processes involved in lineage differentiation as well as sub-typing. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, for projecting cells onto the state space of functional profiles, classifying them according to their principal functions, and reconstructing cell type-specific regulatory networks. Results on sub-typing cancer cells in Melanoma patients reveal novel biomarkers along with their regulatory networks.