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Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Author

Listed:
  • Rongbo Shen

    (Tencent AI Lab)

  • Lin Liu

    (BGI-Shenzhen)

  • Zihan Wu

    (Tencent AI Lab)

  • Ying Zhang

    (BGI-Shenzhen)

  • Zhiyuan Yuan

    (Tencent AI Lab
    Fudan University)

  • Junfu Guo

    (BGI-Shenzhen)

  • Fan Yang

    (Tencent AI Lab)

  • Chao Zhang

    (BGI-Shenzhen)

  • Bichao Chen

    (BGI-Shenzhen)

  • Wanwan Feng

    (Tencent AI Lab
    University of Chinese Academy of Sciences, Chinese Academy of Sciences)

  • Chao Liu

    (BGI-Shenzhen)

  • Jing Guo

    (BGI-Shenzhen)

  • Guozhen Fan

    (BGI-Shenzhen)

  • Yong Zhang

    (BGI-Shenzhen
    Guangdong Bigdata Engineering Technology Research Center for Life Sciences)

  • Yuxiang Li

    (BGI-Shenzhen
    Guangdong Bigdata Engineering Technology Research Center for Life Sciences)

  • Xun Xu

    (BGI-Shenzhen
    Guangdong Provincial Key Laboratory of Genome Read and Write, BGI-Shenzhen)

  • Jianhua Yao

    (Tencent AI Lab)

Abstract

Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.

Suggested Citation

  • Rongbo Shen & Lin Liu & Zihan Wu & Ying Zhang & Zhiyuan Yuan & Junfu Guo & Fan Yang & Chao Zhang & Bichao Chen & Wanwan Feng & Chao Liu & Jing Guo & Guozhen Fan & Yong Zhang & Yuxiang Li & Xun Xu & Ji, 2022. "Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35288-0
    DOI: 10.1038/s41467-022-35288-0
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    References listed on IDEAS

    as
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    Cited by:

    1. Kaichen Xu & Yan Lu & Suyang Hou & Kainan Liu & Yihang Du & Mengqian Huang & Hao Feng & Hao Wu & Xiaobo Sun, 2024. "Detecting anomalous anatomic regions in spatial transcriptomics with STANDS," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    2. Zhiyuan Yuan, 2024. "MENDER: fast and scalable tissue structure identification in spatial omics data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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