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Single-cell chromatin state analysis with Signac

An Author Correction to this article was published on 07 January 2022

This article has been updated


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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Fig. 1: Single-cell chromatin analysis workflow with Signac.
Fig. 2: Integrative single-cell analysis of gene expression and DNA accessibility in human PBMCs.
Fig. 3: Evaluation of dimension reduction methods for single-cell chromatin data.
Fig. 4: Joint analysis of mitochondrial genotypes and DNA accessibility in single cells.
Fig. 5: Scalable analysis of single-cell chromatin data.

Data availability

All data used in the paper are publicly available. The PBMC multiomic dataset is available from 10X Genomics at The PBMC scATAC-seq datasets are available from 10X Genomics at The synthetic scATAC-seq datasets are available from GitHub at Data from the BICCN are available from the Neuroscience Multiomic Archive at Data for the CRC patient sample are available on NCBI Gene Expression Omnibus (GSE148509) and Zenodo (

Code availability

Signac is available on CRAN ( and on GitHub (, with documentation and tutorials available at All code used in this paper is available on GitHub at

Change history


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This work was supported by the Chan Zuckerberg Initiative (EOSS-0000000082 and HCA-A-1704-01895 to R.S.) and the National Institutes of Health (DP2HG009623-01, RM1HG011014-01 and OT2OD026673-01 to R.S.; K99HG011489-01 to T.S.). C.A.L. was supported by a Stanford Science Fellowship. We are grateful to L. Ludwig (MDC Berlin) for insightful conversations about mtDNA lineage tracing. We thank B. Ren (UCSD) for assistance in accessing the BICCN mouse brain dataset. We thank the CRAN maintainers for their assistance in distributing the Signac R package and members of the Satija laboratory for feedback on the manuscript.

Author information

Authors and Affiliations



T.S. and A.S. developed the Signac package with guidance from R.S. R.S. supervised the research. T.S. and S.M. performed analyses. C.A.L. developed the mitochondrial lineage tracing methods and analysis. T.S. wrote the manuscript with input from all authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Tim Stuart or Rahul Satija.

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Competing interests

In the past 3 years, R.S. has worked as a consultant for Bristol-Myers Squibb, Regeneron and Kallyope and served as an SAB member for ImmunAI, Resolve Biosciences, Nanostring, and the NYC Pandemic Response Lab. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Methods thanks Junyue Cao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling editor: Lin Tang, in collaboration with the Nature Methods team.

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Supplementary Table 1

Comparison of single-cell chromatin analysis packages.

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Stuart, T., Srivastava, A., Madad, S. et al. Single-cell chromatin state analysis with Signac. Nat Methods 18, 1333–1341 (2021).

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