Recent advances in single cell genomic technology have enabled the development of high-throughput precision tools to understand cellular function and heterogeneity. The availability of single cell technology overcomes many obstacles associated with bulk tissue measurements where "averaging" over many cells and associated cell states results in a loss of resolution. Using single cell gene expression measurements, computational methods have recently been developed to infer temporal information from single cell experiments where actual time series information cannot be simultaneously acquired. The algorithms attempt to assign a pseudotime to each cell and order them in terms of temporal progression related to the biological process being investigated. However, the major techniques used are deterministic, reporting only a single pseudotemporal ordering. I describe the limitations of such an approach and discuss the utility of novel probabilistic formulations.