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Integrative analyses of single-cell transcriptome and regulome using MAESTRO.
Metadata
Journalgenome biology10.806Date
2020 Aug 07
a month ago
Type
Journal Article
Volume
2020-Aug-07 / 21 : 198
Author
Wang C 1, 2, Sun D 3, Huang X 4, Wan C 3, Li Z 3, Han Y 3, Qin Q 3, Fan J 3, Qiu X 2, 5, Xie Y 2, 5, Meyer CA 1, 2, Brown M 2, 5, Tang M 1, 2, Long H 2, 5, Liu T 6, Liu XS 7, 8
Affiliation
  • 2. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
  • 3. Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Science and Technology, Tongji University, Shanghai, 200433, China.
  • 4. Beijing Institute of Radiation Medicine, Beijing, 100850, China.
  • 5. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.
  • 6. Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA. [email protected]
  • 7. Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA. [email protected]
  • 8. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, 02215, USA. [email protected]
Doi
PMIDMESH
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.
Keywords: Cell-type annotation Computational workflow Integrate scRNA-seq and scATAC-seq Predict transcriptional regulators Single-cell ATAC-seq Single-cell RNA-seq
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Genome Biolgenome biology
Metadata
LocationEngland
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