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Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

Author

Listed:
  • Zhijian Li

    (RWTH Aachen University Medical School)

  • Christoph Kuppe

    (RWTH Aachen University Medical School
    RWTH Aachen University)

  • Susanne Ziegler

    (RWTH Aachen University Medical School)

  • Mingbo Cheng

    (RWTH Aachen University Medical School)

  • Nazanin Kabgani

    (RWTH Aachen University Medical School)

  • Sylvia Menzel

    (RWTH Aachen University Medical School)

  • Martin Zenke

    (RWTH Aachen University Medical School
    RWTH Aachen University)

  • Rafael Kramann

    (RWTH Aachen University Medical School
    RWTH Aachen University
    Erasmus Medical Center)

  • Ivan G. Costa

    (RWTH Aachen University Medical School)

Abstract

A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26530-2
    DOI: 10.1038/s41467-021-26530-2
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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. Eloise Berson & Anjali Sreenivas & Thanaphong Phongpreecha & Amalia Perna & Fiorella C. Grandi & Lei Xue & Neal G. Ravindra & Neelufar Payrovnaziri & Samson Mataraso & Yeasul Kim & Camilo Espinosa & A, 2023. "Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Samir Rachid Zaim & Mark-Phillip Pebworth & Imran McGrath & Lauren Okada & Morgan Weiss & Julian Reading & Julie L. Czartoski & Troy R. Torgerson & M. Juliana McElrath & Thomas F. Bumol & Peter J. Ske, 2024. "MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    5. Irfete S. Fetahu & Wolfgang Esser-Skala & Rohit Dnyansagar & Samuel Sindelar & Fikret Rifatbegovic & Andrea Bileck & Lukas Skos & Eva Bozsaky & Daria Lazic & Lisa Shaw & Marcus Tötzl & Dora Tarlungean, 2023. "Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    6. Hyun Kim & Won Chang & Seok Joo Chae & Jong-Eun Park & Minseok Seo & Jae Kyoung Kim, 2024. "scLENS: data-driven signal detection for unbiased scRNA-seq data analysis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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