Balzer Michael S, Ma Ziyuan, Zhou Jianfu, Abedini Amin, Susztak Katalin
Renal Electrolyte and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.
Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.
J Am Soc Nephrol. 2021 Jun 1;32(6):1279-1292. doi: 10.1681/ASN.2020121742. Epub 2021 Mar 15.
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.
在过去五年中，单细胞方法使得在单个实验中监测数千个单个细胞中的基因和蛋白质表达、遗传及表观遗传变化成为可能。随着测量方法的改进以及反应和测序成本的降低，这些数据集的规模正在迅速增长。关键瓶颈仍然是对单细胞实验产生的大量信息进行分析。在本综述中，我们对当今该领域通常使用的分析流程进行了简化概述。我们旨在让刚开始进行单细胞分析的研究人员能够了解挑战以及最常用的分析工具。此外，我们希望使其他人能够理解单细胞数据集的典型读数在已发表文献中是如何呈现的。
