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.
The architecture was inspired by Spatial-ID.
pip install SpatialIDFor the API, please refer to: https://spatialid.readthedocs.io/en/latest/index.html
- MERFISH: 280,186 cells * 254 genes, 12 samples. https://doi.brainimagelibrary.org/doi/10.35077/g.21
- MERFISH-3D: 213,192 cells * 155 genes, 3 samples. https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248
- Slide-seq: 207,335 cells * 27181 genes, 6 samples. https://www.dropbox.com/s/ygzpj0d0oh67br0/Testis_Slideseq_Data.zip?dl=0
- NanoString: 83,621 cells * 980 genes, 20 samples. https://nanostring.com/resources/smi-ffpe-dataset-lung9-rep1-data/
- Stereo-Seq: Continuous slices of the mouse brain, 3 samples. https://zenodo.org/record/7340795#.Y3xDKrZBy4S
This tool is for research purpose and not approved for clinical use.
This is not an official product.