Single-cell chromatin state analysis with Signac
Open accessAbstract
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.
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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 [143]
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 [143]
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- Authors
- Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R.
- Affiliations
- New York Genome Center (US)
- Stanford University (US)
- Corresponding author
- Stuart, Tim
- Satija, Rahul
- Venue
- Nature Methods
- Published
- 2021-11
- Volume
- 18
- Issue
- 11
- Pages
- 1333-1341
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC9255697
- OpenAlex
- W3210860076
- Cited by
- 1,769
- References
- 82
- Citations by year
- 2026: 449 · 2025: 491 · 2024: 384 · 2023: 277 · 2022: 159 · 2021: 9
- FWCI
- 67.26
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-01-07)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (K99HG011489-01, DP2HG009623-01)
- U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OT2OD026673-01)
- 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
- Signac, chromatin accessibility, peak calling, multimodal single-cell data, dimensionality reduction, clustering, quality control, mitochondrial genotype
- MeSH terms
- Bone Marrow Cells, Chromatin, Humans, Leukocytes, Mononuclear, Mitochondria, Software, Sequence Analysis, DNA, Computational Biology, Gene Expression Profiling, Single-Cell Analysis
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