ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
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Abstract
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.
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Correlation-based methods are used to link peaks to genes within clusters to identify potential CREs.
“To identify potential CREs, correlation-based methods are used to link peaks to genes within clusters of cells140,143,156.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [140]
Binarization of peaks is the most common normalization strategy for scATAC-seq data.
“In scATAC-seq data, the most common normalization strategy is binarization of peaks136,140,141.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [140]
TF-motif enrichment facilitates characterization of cell identity and can be conducted at cluster level using a hypergeometric test on cluster-specific DARs.
“TF-motif enrichment facilitates the characterization of cell identity and can be conducted on a cluster level using a hypergeometric test on cluster-specific DARs140.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [140]
For H3K27ac and H3K4me3, active genes should correspond to high gene activity score.
“For H3K27ac and H3K4me3, active genes should correspond to high GAS2,13 (Methods).”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [13]
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- Authors
- Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J.
- Affiliations
- Stanford Medicine (US)
- Gladstone Institutes (US)
- King Abdulaziz University (SA)
- Howard Hughes Medical Institute (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Granja, Jeffrey M.
- Chang, Howard Y.
- Greenleaf, William J.
- ORCID
- Venue
- Nature Genetics
- Published
- 2021-02-25
- Volume
- 53
- Issue
- 3
- Pages
- 403-411
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC8012210
- OpenAlex
- W3131634425
- Cited by
- 1,392
- References
- 45
- Citations by year
- 2026: 276 · 2025: 343 · 2024: 277 · 2023: 219 · 2022: 176 · 2021: 82 · 2020: 17 · 2013: 1
- FWCI
- 61.77
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-03-31)
- Open access
- hybrid
- doi.org
- Locations
- Nature Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Howard Hughes Medical Institute
- American Society of Hematology (K99-AG059918)
- Gladstone Institutes
- National Institutes of Health (AI057266, AG059918, RM1-HG007735, HG007735)
- National Institute on Aging (K99AG059918)
- National Human Genome Research Institute (RM1HG007735, UM1HG009436, UM1HG009442)
- National Cancer Institute (R35CA209919, U2CCA233311, T32CA009302)
- National Institute of Allergy and Infectious Diseases (U19AI057266)
- Stanford Cancer Institute
- Moonshot Research and Development Program
- Cancer Moonshot
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- single-cell chromatin accessibility, gene expression regulation, doublet removal, single-cell clustering, cell type annotation, multi-omics integration, single-cell RNA sequencing, R software
- MeSH terms
- Animals, Chromatin, Gene Expression Regulation, Humans, Software, Transcription Factors, User-Computer Interface, Cluster Analysis, Genome, Sequence Analysis, RNA, Mice, Single-Cell Analysis, Web Browser
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