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EpiScanpy: integrated single-cell epigenomic analysis

Author

Listed:
  • Anna Danese

    (German Research Center for Environmental Health)

  • Maria L. Richter

    (German Research Center for Environmental Health)

  • Kridsadakorn Chaichoompu

    (German Research Center for Environmental Health)

  • David S. Fischer

    (German Research Center for Environmental Health
    Technical University of Munich)

  • Fabian J. Theis

    (German Research Center for Environmental Health
    Technical University of Munich
    Technical University of Munich)

  • Maria Colomé-Tatché

    (German Research Center for Environmental Health
    Technical University of Munich
    Faculty of Medicine, LMU Munich)

Abstract

EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.

Suggested Citation

  • Anna Danese & Maria L. Richter & Kridsadakorn Chaichoompu & David S. Fischer & Fabian J. Theis & Maria Colomé-Tatché, 2021. "EpiScanpy: integrated single-cell epigenomic analysis," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25131-3
    DOI: 10.1038/s41467-021-25131-3
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    Cited by:

    1. Yichuan Cao & Xiamiao Zhao & Songming Tang & Qun Jiang & Sijie Li & Siyu Li & Shengquan Chen, 2024. "scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Guangyuan Li & Baobao Song & Harinder Singh & V. B. Surya Prasath & H. Leighton Grimes & Nathan Salomonis, 2023. "Decision level integration of unimodal and multimodal single cell data with scTriangulate," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Ariane Lismer & Sarah Kimmins, 2023. "Emerging evidence that the mammalian sperm epigenome serves as a template for embryo development," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    5. Alan Yue Yang Teo & Jordan W. Squair & Gregoire Courtine & Michael A. Skinnider, 2024. "Best practices for differential accessibility analysis in single-cell epigenomics," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    6. Akshaya Ramakrishnan & Aikaterini Symeonidi & Patrick Hanel & Katharina T. Schmid & Maria L. Richter & Michael Schubert & Maria Colomé-Tatché, 2023. "epiAneufinder identifies copy number alterations from single-cell ATAC-seq data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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