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GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations

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  • Tianci Song

    (University of Minnesota Twin Cities)

  • Charles Broadbent

    (University of Minnesota Twin Cities)

  • Rui Kuang

    (University of Minnesota Twin Cities)

Abstract

Spatially-resolved RNA profiling has now been widely used to understand cells’ structural organizations and functional roles in tissues, yet it is challenging to reconstruct the whole spatial transcriptomes due to various inherent technical limitations in tissue section preparation and RNA capture and fixation in the application of the spatial RNA profiling technologies. Here, we introduce a graph-guided neural tensor decomposition (GNTD) model for reconstructing whole spatial transcriptomes in tissues. GNTD employs a hierarchical tensor structure and formulation to explicitly model the high-order spatial gene expression data with a hierarchical nonlinear decomposition in a three-layer neural network, enhanced by spatial relations among the capture spots and gene functional relations for accurate reconstruction from highly sparse spatial profiling data. Extensive experiments on 22 Visium spatial transcriptomics datasets and 3 high-resolution Stereo-seq datasets as well as simulation data demonstrate that GNTD consistently improves the imputation accuracy in cross-validations driven by nonlinear tensor decomposition and incorporation of spatial and functional information, and confirm that the imputed spatial transcriptomes provide a more complete gene expression landscape for downstream analyses of cell/spot clustering for tissue segmentation, and spatial gene expression clustering and visualizations.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44017-0
    DOI: 10.1038/s41467-023-44017-0
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    References listed on IDEAS

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    1. 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.
    2. Kangning Dong & Shihua Zhang, 2022. "Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Yahui Long & Kok Siong Ang & Mengwei Li & Kian Long Kelvin Chong & Raman Sethi & Chengwei Zhong & Hang Xu & Zhiwei Ong & Karishma Sachaphibulkij & Ao Chen & Li Zeng & Huazhu Fu & Min Wu & Lina Hsiu Ki, 2023. "Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
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