How to Get Started with Single Cell RNA Sequencing Data Analysis

Over the last 5 years, single cell methods have enabled the monitoring of gene and protein expression, genetic, and epigenetic changes in thousands of individual cells in a single experiment. With the improved measurement and the decreasing cost of the reactions and sequencing, the size of these datasets is increasing rapidly. The critical bottleneck remains the analysis of the wealth of information generated by single cell experiments. In this review, we give a simplified overview of the analysis pipelines, as they are typically used in the field today. We aim to enable researchers starting out in single cell analysis to gain an overview of challenges and the most commonly used analytical tools. In addition, we hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.

Automated summary

Single cell methods have advanced significantly in the past few years, enabling the monitoring of gene and protein expression changes in individual cells. However, data analysis remains a critical bottleneck. This review provides an overview of commonly used analytical tools in the field to help researchers understand challenges and gain insights into typical readouts from single cell datasets in published literature.