Nature Communications (Dec 2022)

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,
  • Jianhua Yao

DOI
https://doi.org/10.1038/s41467-022-35288-0
Journal volume & issue
Vol. 13, no. 1
pp. 1 – 17

Abstract

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Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here the authors introduce 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.