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

Shen, R.; Liu, L.; Wu, Z.; Zhang, Y.; Yuan, Z.; guo, j.; Yang, F.; Zhang, C.; Chen, B.; Liu, C.; Guo, J.; Fan, G.; Li, Y.; Xu, X.; Yao, J.

doi:10.1101/2022.05.26.493527

bioRxiv · 10.1101/2022.05.26.493527

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

Shen, R.·Liu, L.·Wu, Z.·Zhang, Y.·Yuan, Z.·guo, j.·Yang, F.·Zhang, C.·Chen, B.·Liu, C.·Guo, J.·Fan, G.·Li, Y.·Xu, X.·Yao, J.

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Abstract

Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets. A series of quantitative comparison experiments on public available SRT datasets demonstrate the superiority of Spatial-ID compared with other state-of-the-art methods. Besides, the application of Spatial-ID on a SRT dataset with 3D spatial dimension measured by Stereo-seq shows its advancement on the large field tissues with subcellular spatial resolution.

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Shen, R., Liu, L., Wu, Z., Zhang, Y., Yuan, Z., guo, j., Yang, F., Zhang, C., Chen, B., Liu, C., Guo, J., Fan, G., Li, Y., Xu, X., Yao, J.. 2022-05-27. Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. https://doi.org/10.1101/2022.05.26.493527

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