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Transformer for one stop interpretable cell type annotation

Author

Listed:
  • Jiawei Chen

    (Peking University)

  • Hao Xu

    (Peking University)

  • Wanyu Tao

    (Peking University)

  • Zhaoxiong Chen

    (Peking University)

  • Yuxuan Zhao

    (Peking University)

  • Jing-Dong J. Han

    (Peking University)

Abstract

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA’s advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity.

Suggested Citation

  • Jiawei Chen & Hao Xu & Wanyu Tao & Zhaoxiong Chen & Yuxuan Zhao & Jing-Dong J. Han, 2023. "Transformer for one stop interpretable cell type annotation," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-35923-4
    DOI: 10.1038/s41467-023-35923-4
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    References listed on IDEAS

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    1. Zhi-Jie Cao & Lin Wei & Shen Lu & De-Chang Yang & Ge Gao, 2020. "Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. Chenwei Li & Baolin Liu & Boxi Kang & Zedao Liu & Yedan Liu & Changya Chen & Xianwen Ren & Zemin Zhang, 2020. "SciBet as a portable and fast single cell type identifier," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
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    Cited by:

    1. Olga Guselnikova & Andrii Trelin & Yunqing Kang & Pavel Postnikov & Makoto Kobashi & Asuka Suzuki & Lok Kumar Shrestha & Joel Henzie & Yusuke Yamauchi, 2024. "Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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