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.
Highlights
Cells in a multicellular organism may display tremendous transcriptomic and epigenetic heterogeneities
Summary result and HTML output For users to better understand the results from the Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO) workflow, we provide output files in HTML format to summarize the mapping statistics, quality control analysis from RseQC, single-cell QC plot, clustering result, cell-type annotation result, and transcription regulator predictions from LISA or GIGGLE
The description of each result and the normal range of QC metrics are included in the HTML output
Summary
Cells in a multicellular organism may display tremendous transcriptomic and epigenetic heterogeneities. Recent advances in single-cell technologies enabled the measurements of gene expression and chromatin accessibility at a singlecell resolution using scRNA-seq and scATAC-seq [5,6,7,8]. They provided unprecedented opportunities to investigate the complex gene regulation mechanisms underlying immune response [9, 10], brain function [11], tumor heterogeneity [12], and. Wang et al Genome Biology (2020) 21:198 developmental plasticity [13, 14] These technologies generate large volumes of data, which pose significant computational challenges
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