ratschlab/DeepSpot
DeepSpot: Deep learning model for predicting spatial transcriptomics from H&E histopathology images. Supports spot-level (Visium) and single-cell (Xenium) resolution.
DeepSpot helps cancer researchers and pathologists predict detailed gene expression patterns directly from standard H&E stained tissue images. You provide an H&E histopathology image, and the system outputs a map of spatial gene expression, showing which genes are active in different areas of the tissue. This allows for virtual spatial transcriptomics analysis without needing additional specialized lab procedures.
Use this if you need to understand gene expression at a spot or single-cell level within tissue samples, but only have H&E images available or want to reduce the cost and complexity of generating spatial transcriptomics data.
Not ideal if you already have direct spatial transcriptomics data and are looking for advanced analysis tools, rather than a prediction method from H&E images.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 09, 2026
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