Integrative analyses of single-cell transcriptome and regulome using MAESTRO

• Main Authors: Chenfei Wang, Dongqing Sun, Xin Huang, Changxin Wan, Ziyi Li, Ya Han, Qian Qin, Jingyu Fan, Xintao Qiu, Yingtian Xie, Clifford A. Meyer, Myles Brown, Ming Tang, Henry Long, Tao Liu, X. Shirley Liu
• Format: Article
• Language: English
• Published: BMC 2020-08-01
• Series: Genome Biology
• Subjects: Single-cell RNA-seq; Single-cell ATAC-seq; Computational workflow; Integrate scRNA-seq and scATAC-seq; Cell-type annotation; Predict transcriptional regulators
• Online Access: http://link.springer.com/article/10.1186/s13059-020-02116-x

Abstract 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 ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, 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 scRNA-seq 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.