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Unsupervised clustering and epigenetic classification of single cells

Author

Listed:
  • Mahdi Zamanighomi

    (Stanford University)

  • Zhixiang Lin

    (Stanford University)

  • Timothy Daley

    (Stanford University
    Stanford University)

  • Xi Chen

    (Stanford University
    Stanford University)

  • Zhana Duren

    (Stanford University)

  • Alicia Schep

    (Stanford University School of Medicine
    Stanford University)

  • William J. Greenleaf

    (Stanford University School of Medicine
    Stanford University
    Stanford University)

  • Wing Hung Wong

    (Stanford University
    Stanford University)

Abstract

Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.

Suggested Citation

  • Mahdi Zamanighomi & Zhixiang Lin & Timothy Daley & Xi Chen & Zhana Duren & Alicia Schep & William J. Greenleaf & Wing Hung Wong, 2018. "Unsupervised clustering and epigenetic classification of single cells," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04629-3
    DOI: 10.1038/s41467-018-04629-3
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    Cited by:

    1. 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.
    2. Wenjing Ma & Jiaying Lu & Hao Wu, 2023. "Cellcano: supervised cell type identification for single cell ATAC-seq data," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. Yunfan Li & Dan Zhang & Mouxing Yang & Dezhong Peng & Jun Yu & Yu Liu & Jiancheng Lv & Lu Chen & Xi Peng, 2023. "scBridge embraces cell heterogeneity in single-cell RNA-seq and ATAC-seq data integration," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Tianqi Liu & Yu Lu & Biqing Zhu & Hongyu Zhao, 2023. "Clustering high‐dimensional data via feature selection," Biometrics, The International Biometric Society, vol. 79(2), pages 940-950, June.
    6. 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.

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