ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

By Jeffrey M. Granja, M. Ryan, M. Corces, Sarah E. Pierce, Tansu Bagdatli, Hani Choudhry, Howard Y. Chang, W.J. Greenleaf. Nature Genetics, 2021

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The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells. ArchR is a software suite that enables efficient and end-to-end analysis of single-cell chromatin accessibility data (scATAC-seq).

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f0dda1d1111250817f30c467d95274414ea0fdf5

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Citations in the corpus

Best practices for single-cell analysis across modalities (2023, methodology): “Annotation of cell clusters can be performed on the basis of differentially accessible regions (DARs) and gene activity scores (Fig.”
Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “Thus, treating chromatin accessibility data as binary might be detrimental for downstream analyses 160,161 , and methods that handle accessibility in a quantitative manner might improve GRN reconstruction 154,155,162,163 .”
Methods and applications for single-cell and spatial multi-omics (2023, methodology): “This approach was used, for example, by Foster et al., using ArchR 152 for scRNA-seq and scATAC-seq integration, to identify spatial fibroblast epigenome patterns during tissue repair 153 .”
Single-cell chromatin state analysis with Signac (2021, methodology): “We also compared the runtime for Signac to the recently published ArchR package 35 for equivalent analysis steps.”
Benchmarking atlas-level data integration in single-cell genomics (2020, background): “While alternative choices of gene activity scoring may improve this feature space 33,34 , our results indicate that peaks or windows are better suited to integrate and analyze scATAC-seq data.”