计算生物学
仿形(计算机编程)
数据集成
模式
计算机科学
转录组
组学
蛋白质组
系统生物学
生物
生物信息学
数据挖掘
基因
遗传学
基因表达
操作系统
社会学
社会科学
作者
Mirjana Efremova,Sarah A. Teichmann
出处
期刊:Nature Methods
[Springer Nature]
日期:2020-01-01
卷期号:17 (1): 14-17
被引量:158
标识
DOI:10.1038/s41592-019-0692-4
摘要
Single-cell omics approaches provide high-resolution data on cellular phenotypes, developmental dynamics and communication networks in diverse tissues and conditions. Emerging technologies now measure different modalities of individual cells, such as genomes, epigenomes, transcriptomes and proteomes, in addition to spatial profiling. Combined with analytical approaches, these data open new avenues for accurate reconstruction of gene-regulatory and signaling networks driving cellular identity and function. Here we summarize computational methods for analysis and integration of single-cell omics data across different modalities and discuss their applications, challenges and future directions.
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