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.
Researchers from the University of Pennsylvania, Perelman School of Medicine give a simplified overview of the analysis pipelines, as they are typically used in the field today. They 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, the researchers hope to empower others to gain an understanding of how typical readouts from single cell datasets are presented in the published literature.