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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Author

Listed:
  • Rongxin Fang

    (Ludwig Institute for Cancer Research
    Harvard University)

  • Sebastian Preissl

    (University of California, San Diego)

  • Yang Li

    (Ludwig Institute for Cancer Research)

  • Xiaomeng Hou

    (University of California, San Diego)

  • Jacinta Lucero

    (The Salk Institute for Biological Studies)

  • Xinxin Wang

    (University of California, San Diego)

  • Amir Motamedi

    (Ludwig Institute for Cancer Research)

  • Andrew K. Shiau

    (Ludwig Institute for Cancer Research)

  • Xinzhu Zhou

    (University of California San Diego)

  • Fangming Xie

    (University of California, San Diego)

  • Eran A. Mukamel

    (University of California, San Diego)

  • Kai Zhang

    (Ludwig Institute for Cancer Research)

  • Yanxiao Zhang

    (Ludwig Institute for Cancer Research)

  • M. Margarita Behrens

    (The Salk Institute for Biological Studies)

  • Joseph R. Ecker

    (The Salk Institute for Biological Studies
    The Salk Institute for Biological Studies)

  • Bing Ren

    (Ludwig Institute for Cancer Research
    University of California, San Diego
    Institute of Genomic Medicine, UCSD Moores Cancer Center)

Abstract

Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

Suggested Citation

  • Rongxin Fang & Sebastian Preissl & Yang Li & Xiaomeng Hou & Jacinta Lucero & Xinxin Wang & Amir Motamedi & Andrew K. Shiau & Xinzhu Zhou & Fangming Xie & Eran A. Mukamel & Kai Zhang & Yanxiao Zhang & , 2021. "Comprehensive analysis of single cell ATAC-seq data with SnapATAC," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21583-9
    DOI: 10.1038/s41467-021-21583-9
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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. Shengen Shawn Hu & Lin Liu & Qi Li & Wenjing Ma & Michael J. Guertin & Clifford A. Meyer & Ke Deng & Tingting Zhang & Chongzhi Zang, 2022. "Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    3. Seong Kyu Han & Michelle T. McNulty & Christopher J. Benway & Pei Wen & Anya Greenberg & Ana C. Onuchic-Whitford & Dongkeun Jang & Jason Flannick & Noël P. Burtt & Parker C. Wilson & Benjamin D. Humph, 2023. "Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Sudha Sunil Rajderkar & Kitt Paraiso & Maria Luisa Amaral & Michael Kosicki & Laura E. Cook & Fabrice Darbellay & Cailyn H. Spurrell & Marco Osterwalder & Yiwen Zhu & Han Wu & Sarah Yasmeen Afzal & Ma, 2024. "Dynamic enhancer landscapes in human craniofacial development," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Parker C. Wilson & Yoshiharu Muto & Haojia Wu & Anil Karihaloo & Sushrut S. Waikar & Benjamin D. Humphreys, 2022. "Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
    6. 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.
    7. Kai Cao & Qiyu Gong & Yiguang Hong & Lin Wan, 2022. "A unified computational framework for single-cell data integration with optimal transport," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    8. Christopher T. Rhodes & Joyce J. Thompson & Apratim Mitra & Dhanya Asokumar & Dongjin R. Lee & Daniel J. Lee & Yajun Zhang & Eva Jason & Ryan K. Dale & Pedro P. Rocha & Timothy J. Petros, 2022. "An epigenome atlas of neural progenitors within the embryonic mouse forebrain," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    9. Yuki Matsushita & Jialin Liu & Angel Ka Yan Chu & Chiaki Tsutsumi-Arai & Mizuki Nagata & Yuki Arai & Wanida Ono & Kouhei Yamamoto & Thomas L. Saunders & Joshua D. Welch & Noriaki Ono, 2023. "Bone marrow endosteal stem cells dictate active osteogenesis and aggressive tumorigenesis," Nature Communications, Nature, vol. 14(1), pages 1-23, December.
    10. Lei Xiong & Kang Tian & Yuzhe Li & Weixi Ning & Xin Gao & Qiangfeng Cliff Zhang, 2022. "Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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