Comprehensive analysis of single cell ATAC-seq data with SnapATAC
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Abstract
Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.
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Chromatin accessibility of CREs associated with a gene can be summarized into gene activity scores by summing counts within genes and a certain distance upstream of the TSS.
“This can be achieved by summing up counts within genes and a certain distance upstream of the TSS136,143,150.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [136]
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 [136]
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Self-citationA cell-type-specific error-correction signal in the posterior parietal cortex
Green, J., Bruno, C. A., Traunmüller, L., Ding, J., Hrvatin, S., Wilson, D. E., Khodadad, T., Samuels, J., Greenberg, M. E., & Harvey, C. D. (2023) · Nature · doi:10.1038/s41586-023-06357-1 · Cited by 43 · see citing works
Single cell epigenomic atlas of the developing human brain and organoids
Ziffra, R. S., Kim, C. N., Wilfert, A., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Kreimer, A., Pollard, K. S., Ament, S. A., et al. (2019) · doi:10.1101/2019.12.30.891549 · Cited by 33 · see citing works
Enhanced integration of single-cell multi-omics data using graph attention networks
Liao, X., Li, Y., Li, S., Wen, L., Li, X., & Yu, B. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00864 · Cited by 5 · see citing works
Self-citation
- Authors
- Fang, R., Preissl, S., Li, Y., Hou, X., Lucero, J., Wang, X., Motamedi, A., Shiau, A. K., Zhou, X., Xie, F., Mukamel, E. A., Zhang, K., Zhang, Y., Behrens, M. M., Ecker, J. R., & Ren, B.
- Affiliations
- Harvard University (US)
- University of California San Diego (US)
- Ludwig Cancer Research (US)
- Salk Institute for Biological Studies (US)
- Ludwig Cancer Research (BE)
- Corresponding author
- Ren, Bing
- ORCID
- Venue
- Nature Communications
- Published
- 2021-02-26
- Volume
- 12
- Issue
- 1
- Pages
- 1337-1337
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7910485
- OpenAlex
- W3133082627
- Cited by
- 455
- References
- 58
- Citations by year
- 2026: 41 · 2025: 63 · 2024: 94 · 2023: 78 · 2022: 88 · 2021: 65 · 2020: 17 · 2019: 9
- FWCI
- 27.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- Howard Hughes Medical Institute
- Damon Runyon Cancer Research Foundation
- National Institutes of Health
- University of California, San Diego
- National Institute of Mental Health (U19MH114831)
- National Cancer Institute (K99CA252020, P30CA014195)
- School of Medicine, University of California, San Diego
- 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 › Gene Regulatory Network Analysis
- Keywords
- single-cell ATAC-seq, cis-regulatory elements, open chromatin, cellular heterogeneity, cell type-specific gene expression, single-nucleus ATAC-seq, secondary motor cortex, transcription factors, cell trajectory inference, Nyström method, software package
- MeSH terms
- Chromatin Immunoprecipitation Sequencing, Animals, Chromatin, Male, Mice, Inbred C57BL, Motor Cortex, Sequence Analysis, DNA, Computational Biology, Mice, Epigenomics, Single-Cell Analysis
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