Integrative analyses of single-cell transcriptome and regulome using MAESTRO.
Cell-type annotation
Computational workflow
Integrate scRNA-seq and scATAC-seq
Predict transcriptional regulators
Single-cell ATAC-seq
Single-cell RNA-seq
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
07 08 2020
07 08 2020
Historique:
received:
06
12
2019
accepted:
23
07
2020
entrez:
10
8
2020
pubmed:
10
8
2020
medline:
9
7
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32767996
doi: 10.1186/s13059-020-02116-x
pii: 10.1186/s13059-020-02116-x
pmc: PMC7412809
doi:
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
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