瓶颈
单细胞分析
计算生物学
数据科学
核糖核酸
领域(数学)
表观遗传学
生物
数据挖掘
计算机科学
细胞
生物信息学
基因
遗传学
嵌入式系统
纯数学
数学
作者
Michael S. Balzer,Ziyuan Ma,Jianfu Zhou,Amin Abedini,Katalin Suszták
出处
期刊:Journal of The American Society of Nephrology
日期:2021-06-01
卷期号:32 (6): 1279-1292
被引量:25
标识
DOI:10.1681/asn.2020121742
摘要
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.
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