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

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 () and Jianhua Yao ()
Additional contact information
Rongbo Shen: Tencent AI Lab
Lin Liu: BGI-Shenzhen
Zihan Wu: Tencent AI Lab
Ying Zhang: BGI-Shenzhen
Zhiyuan Yuan: Tencent AI Lab
Junfu Guo: BGI-Shenzhen
Fan Yang: Tencent AI Lab
Chao Zhang: BGI-Shenzhen
Bichao Chen: BGI-Shenzhen
Wanwan Feng: Tencent AI Lab
Chao Liu: BGI-Shenzhen
Jing Guo: BGI-Shenzhen
Guozhen Fan: BGI-Shenzhen
Yong Zhang: BGI-Shenzhen
Yuxiang Li: BGI-Shenzhen
Xun Xu: BGI-Shenzhen
Jianhua Yao: Tencent AI Lab

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: 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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35288-0

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DOI: 10.1038/s41467-022-35288-0

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