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Single-cell chromatin state analysis with Signac

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

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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.

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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

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  1. Ai, S. et al. Profiling chromatin states using single-cell itChIP-seq. Nat. Cell Biol. 21, 1164–1172 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Carter, B. et al. Mapping histone modifications in low cell number and single cells using antibody-guided chromatin tagmentation (ACT-seq). Nat. Commun. 10, 3747 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wang, Q. et al. CoBATCH for high-throughput single-cell epigenomic profiling. Mol. Cell (2019).

  7. Ku, W. L. et al. Single-cell chromatin immunocleavage sequencing (scChIC-seq) to profile histone modification. Nat. Methods 16, 323–325 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. (2019).

  9. Luo, C. et al. Robust single-cell DNA methylome profiling with snmc-seq2. Nat. Commun. 9, 3824 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science (2018).

  13. Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. (2019).

  14. Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and Single-Cell genomics. Cell (2019).

  16. Lareau, C. A. et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. (2021).

  17. Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Xing, Q. R. et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res. 30, 1027–1039 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Liu, L. et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10, 470 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ma, S. et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell (2020).

  21. Mimitou, E. P. et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat. Biotechnol. (2021).

  22. Fiskin, E., Lareau, C. A., Eraslan, G., Ludwig, L. S. & Regev, A. Single-cell multimodal profiling of proteins and chromatin accessibility using PHAGE-ATAC. Preprint at BioRxiv (2020).

  23. Swanson, E. et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. eLife 10, e63632 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376 (2019).

    Article  Google Scholar 

  25. Pierce, S. E., Granja, J. M. & Greenleaf, W. J. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat. Commun. 12, 2969 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Thornton, C. A. et al. Spatially mapped single-cell chromatin accessibility. Nat. Commun. 12, 1274 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. (2019).

  28. Bravo González-Blas, C. et al. cistopic: cis-regulatory topic modeling on single-cell ATAC-seq data. Nat. Methods (2019).

  29. Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018).

    Article  Google Scholar 

  30. Xiong, L. et al. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat. Commun. 10, 4576 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from Single-Cell chromatin accessibility data. Mol. Cell 71, 858–871.e8 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Danese, A. et al. EpiScanpy: integrated single-cell epigenomic analysis. Nat.Commun. (2021).

  34. Fang, R. et al. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. (2021).

  36. Ji, Z., Zhou, W. & Ji, H. Single-cell regulome data analysis by SCRAT. Bioinformatics 33, 2930–2932 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Baker, S. M., Rogerson, C., Hayes, A., Sharrocks, A. D. & Rattray, M. Classifying cells with scasat, a single-cell ATAC-seq analysis tool. Nucleic Acids Res. 47, e10 (2019).

    Article  PubMed  Google Scholar 

  38. Zhao, C., Hu, S., Huo, X. & Zhang, Y. Dr.seq2: a quality control and analysis pipeline for parallel single cell transcriptome and epigenome data. PLoS ONE 12, e0180583 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. (2018).

  41. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  Google Scholar 

  42. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

    Article  Google Scholar 

  43. Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife (2019).

  44. Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K. & Harshman, R. Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391–407 (1990).

    Article  Google Scholar 

  48. McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv (2018).

  49. Pearce, E. L. et al. Control of effector CD8+ T cell function by the transcription factor eomesodermin. Science 302, 1041–1043 (2003).

    Article  CAS  PubMed  Google Scholar 

  50. Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science (2018).

  51. GTEx Consortium. The GTEx Consortium Atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article  Google Scholar 

  52. Chen, H. et al. Assessment of computational methods for the analysis of single-cell ATAC-seq data. Genome Biol. 20, 241 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Li, Y. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Preprint at bioRxiv (2020).

  54. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature (2019).

  55. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat. Methods (2019).

  56. Brenner, S. Sequences and consequences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 207–212 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Richmond, T. J. & Davey, C. A. The structure of DNA in the nucleosome core. Nature 423, 145–150 (2003).

    Article  CAS  PubMed  Google Scholar 

  58. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  60. Baglama, J. & Reichel, L. Augmented implicitly restarted Lanczos bidiagonalization methods. SIAM J. Sci. Comput. 27, 19–42 (2005).

    Article  Google Scholar 

  61. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).

    Article  Google Scholar 

  63. Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. E. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R. J. 8, 289–317 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).

    Article  CAS  PubMed  Google Scholar 

  65. Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2020).

    CAS  PubMed  Google Scholar 

  66. Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B. & Eskin, E. Identifying causal variants at loci with multiple signals of association. Genetics 198, 497–508 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv (2013).

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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.

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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.

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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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