Single cell RNA sequencing (scRNA-seq) technology is a revolutionary tool which allows scientists to measure the activity of genes in thousands of individual cells simultaneously. With this technology, we can start to look at what each cell in a sample is doing instead of considering an average value across all cells in a sample, as was done in a bulk cell RNA-Seq experiment. Consequently, it reduces the chances of masking the cellular heterogeneity in the process. Even within the same cellular sub population, genes can be highly expressed in some cells but not expressed (or lowly expressed) in others. Therefore, the computational approaches used to analyze bulk RNA sequencing data are not appropriate for the analysis of scRNA-seq data. There are several aspects of analyzing scRNA-Seq data. In this work we will present a brief summary of statistical issues of different computational algorithms for identifying differentially expressed genes in scRNA-Seq data. Additionally, we will present our novel statistical model for high dimensional and zero inflated scRNA-seq count data to identify differentially expressed genes across cell types.