The popularity and use of single cell RNA sequencing (scRNA-seq) experiments has grown exponentially over the past few years as researchers are now able to examine gene expression at the level of a single cell. However, intrinsic properties of this data, such as bimodal distributions of gene expression and cell-to-cell heterogeneity within the same cellular population, pose computational and statistical challenges not previously faced with traditional transcriptomic data averaged over bulk populations of cells. To that end, new analytical techniques still need to be developed for scRNA-seq studies. This talk introduces a hurdle model for high dimensional and zero-inflated scRNA-seq count data to detect differentially expressed genes across cell types. The centerpiece of this model is a correlated random effects structure that is guided by an initial supervised subpopulation clustering assignment to capture the observed cellular variability within treatment groups of cells. Furthermore, an approach to combine the results of this hurdle model and ultimately identify differentially expressed genes is presented.