Integrative analyses of single-cell transcriptome and regulome using MAESTRO.
Chenfei WangDongqing SunXin HuangChangxin WanZiyi LiYa HanQian QinJingyu FanXintao QiuYingtian XieClifford A MeyerMyles BrownMing TangHenry LongTao LiuX Shirley LiuPublished in: Genome biology (2020)
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.
Keyphrases
- rna seq
- single cell
- genome wide
- high throughput
- quality control
- transcription factor
- gene expression
- dna damage
- poor prognosis
- stem cells
- dna methylation
- machine learning
- electronic health record
- deep learning
- big data
- risk assessment
- cell therapy
- genome wide identification
- artificial intelligence
- bone marrow
- network analysis