ratschlab/DeepSpot2Cell
DeepSpot2Cell: Predicting virtual single-cell spatial transcriptomics from H&E images using spot-level supervision
This project helps pathologists and cancer researchers analyze tissue samples by predicting detailed gene expression at a single-cell level directly from standard H&E stained images. You input an H&E image, and it outputs a 'virtual' spatial transcriptomic profile for each cell. This allows scientists to understand cellular behavior and disease progression without needing specialized, expensive spatial transcriptomics experiments for every sample.
Use this if you need to infer single-cell spatial gene expression from routine H&E pathology images to enhance your understanding of tissue microenvironments and disease.
Not ideal if you require actual, experimentally-derived spatial transcriptomics data rather than computational predictions.
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8
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 08, 2026
Commits (30d)
0
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