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A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics

Author

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  • Haoyang Li

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Juexiao Zhou

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Zhongxiao Li

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Siyuan Chen

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Xingyu Liao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Bin Zhang

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

  • Ruochi Zhang

    (Syneron Technology)

  • Yu Wang

    (Syneron Technology)

  • Shiwei Sun

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xin Gao

    (King Abdullah University of Science and Technology (KAUST)
    King Abdullah University of Science and Technology (KAUST))

Abstract

Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.

Suggested Citation

  • Haoyang Li & Juexiao Zhou & Zhongxiao Li & Siyuan Chen & Xingyu Liao & Bin Zhang & Ruochi Zhang & Yu Wang & Shiwei Sun & Xin Gao, 2023. "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37168-7
    DOI: 10.1038/s41467-023-37168-7
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    References listed on IDEAS

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    Cited by:

    1. Jingyang Qian & Hudong Bao & Xin Shao & Yin Fang & Jie Liao & Zhuo Chen & Chengyu Li & Wenbo Guo & Yining Hu & Anyao Li & Yue Yao & Xiaohui Fan & Yiyu Cheng, 2024. "Simulating multiple variability in spatially resolved transcriptomics with scCube," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    2. Tianci Song & Charles Broadbent & Rui Kuang, 2023. "GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Haoyang Li & Yingxin Lin & Wenjia He & Wenkai Han & Xiaopeng Xu & Chencheng Xu & Elva Gao & Hongyu Zhao & Xin Gao, 2024. "SANTO: a coarse-to-fine alignment and stitching method for spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    4. Brian S. White & Aurélien Reyniès & Aaron M. Newman & Joshua J. Waterfall & Andrew Lamb & Florent Petitprez & Yating Lin & Rongshan Yu & Martin E. Guerrero-Gimenez & Sergii Domanskyi & Gianni Monaco &, 2024. "Community assessment of methods to deconvolve cellular composition from bulk gene expression," Nature Communications, Nature, vol. 15(1), pages 1-22, December.

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