SPACEL: deep learning-based characterization of spatial transcriptome architectures

Hao Xu, Shuyan Wang, Minghao Fang, Songwen Luo, Chunpeng Chen, Siyuan Wan, Rirui Wang, Meifang Tang, Tian Xue, Bin Li (), Jun Lin () and Kun Qu ()
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Hao Xu: University of Science and Technology of China
Shuyan Wang: Hefei Comprehensive National Science Center
Minghao Fang: Hefei Comprehensive National Science Center
Songwen Luo: University of Science and Technology of China
Chunpeng Chen: University of Science and Technology of China
Siyuan Wan: Hefei Comprehensive National Science Center
Rirui Wang: University of Science and Technology of China
Meifang Tang: University of Science and Technology of China
Tian Xue: University of Science and Technology of China
Bin Li: National Institute of Biological Sciences
Jun Lin: University of Science and Technology of China
Kun Qu: University of Science and Technology of China

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Spatial transcriptomics (ST) technologies detect mRNA expression in single cells/spots while preserving their two-dimensional (2D) spatial coordinates, allowing researchers to study the spatial distribution of the transcriptome in tissues; however, joint analysis of multiple ST slices and aligning them to construct a three-dimensional (3D) stack of the tissue still remain a challenge. Here, we introduce spatial architecture characterization by deep learning (SPACEL) for ST data analysis. SPACEL comprises three modules: Spoint embeds a multiple-layer perceptron with a probabilistic model to deconvolute cell type composition for each spot in a single ST slice; Splane employs a graph convolutional network approach and an adversarial learning algorithm to identify spatial domains that are transcriptomically and spatially coherent across multiple ST slices; and Scube automatically transforms the spatial coordinate systems of consecutive slices and stacks them together to construct a 3D architecture of the tissue. Comparisons against 19 state-of-the-art methods using both simulated and real ST datasets from various tissues and ST technologies demonstrate that SPACEL outperforms the others for cell type deconvolution, for spatial domain identification, and for 3D alignment, thus showcasing SPACEL as a valuable integrated toolkit for ST data processing and analysis.

Date: 2023
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DOI: 10.1038/s41467-023-43220-3

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