染色质
计算机科学
计算生物学
可视化
细胞
生物
染色质重塑
DNA
数据挖掘
嘉雅宠物
遗传学
作者
Tim Stuart,Avi Srivastava,Shaista Madad,Caleb A. Lareau,Rahul Satija
出处
期刊:Nature Methods
[Springer Nature]
日期:2021-11-01
卷期号:18 (11): 1333-1341
被引量:834
标识
DOI:10.1038/s41592-021-01282-5
摘要
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells. The Signac framework enables the end-to-end analysis of single-cell chromatin data and interoperability with the Seurat package for multimodal analysis.
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