Abstract
Single-cell RNA sequencing (scRNA-seq) is a promising approach to study the transcriptomes of individual cells in the brain and the central nervous system (CNS). This technology acts as a bridge between neuroscience, computational biology, and systems biology, enabling an unbiased and novel understanding of the cellular composition of the brain and CNS. Gene expression at the single cell resolution is often noisy, sparse, and high-dimensional, creating challenges for computational analysis of such data. In this review, we overview fundamental sample preparation and data analysis processes of scRNA-seq and provide a comparative perspective for analyzing and visualizing these data.