This is a modern fork with updated versions of Python and related dependencies, and uses uv.

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 git+https://github.com/myuanz/SpatialID.git

For 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.