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SCALE method for single-cell ATAC-seq analysis via latent feature extraction

Author

Listed:
  • Lei Xiong

    (Tsinghua University)

  • Kui Xu

    (Tsinghua University)

  • Kang Tian

    (Tsinghua University)

  • Yanqiu Shao

    (Tsinghua University)

  • Lei Tang

    (Tsinghua University)

  • Ge Gao

    (Peking University
    Peking University)

  • Michael Zhang

    (Tsinghua University
    The University of Texas
    Tsinghua University)

  • Tao Jiang

    (University of California
    Tsinghua University)

  • Qiangfeng Cliff Zhang

    (Tsinghua University)

Abstract

Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments.

Suggested Citation

  • Lei Xiong & Kui Xu & Kang Tian & Yanqiu Shao & Lei Tang & Ge Gao & Michael Zhang & Tao Jiang & Qiangfeng Cliff Zhang, 2019. "SCALE method for single-cell ATAC-seq analysis via latent feature extraction," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12630-7
    DOI: 10.1038/s41467-019-12630-7
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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. 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.
    3. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. 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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