Over the last decade, single cell RNA sequencing (scRNAseq) became an increasingly viable solution for analyzing cellular heterogeneity and cell-specific expression differences. While not as mature or fully realized as bulk sequencing, newly developed computational methods offer a solution to the challenges of scRNAseq data analysis, providing previously inaccessible biological insight at unprecedented levels of detail. Here, we go over the inherent challenges of single-cell data analysis and the computational methods used to overcome them. We cover current and future applications of scRNAseq in research of cellular dynamics and as an integrative component of biological research.
Keywords: Dimensionality reduction; Gene expression; Next-generation sequencing; R; Single-cell sequencing.