Integrative analyses of single-cell transcriptome and regulome using MAESTRO
Integrative analyses of single-cell transcriptome and regulome using MAESTRO
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DOI：
10.1186/s13059-020-02116-x
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发表时间：
2020-08-07
期刊：
影响因子：
12.3
通讯作者：
Liu, X. Shirley
中科院分区：
文献类型：
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作者：
Wang, Chenfei;Sun, Dongqing;Liu, X. Shirley
我们提出基于模型的转录组和 RegulOme 分析 (MAESTRO)，这是一个全面的开源计算工作流程 (http://github.com/liulab-dfci/ MAESTRO)，用于对来自多个平台的单细胞 RNA-seq (scRNA-seq) 和 ATACseq (scATAC-seq) 数据进行综合分析。 MAESTRO 提供预处理、比对、质量控制、表达和染色质可及性定量、聚类、差异分析和注释等功能。通过在单细胞水平上对染色质可及性的基因调控潜力进行建模，MAESTRO 优于在 scRNAseq 和 scATAC-seq 之间整合细胞簇的现有方法。此外，MAESTRO 支持使用预定义的细胞类型标记基因进行自动细胞类型注释，并从差异 scRNA-seq 基因和 scATAC-seq 峰中识别驱动调节因子。
We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow (http://github.com/liulab-dfci/ MAESTRO) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATACseq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for preprocessing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNAseq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC- seq peaks.