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Integrative analyses of single-cell transcriptome and regulome using MAESTRO

  • 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
  • Harvard University
  • Dana-Farber Cancer Institute
  • Tongji University
  • Academy of Military Medical Science China

Research output: Contribution to journalArticlepeer-review

147 Scopus citations

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.

Original languageEnglish
Article number198
JournalGenome Biology
Volume21
Issue number1
DOIs
StatePublished - Aug 7 2020

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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