ys-zong/conST
conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics
This project helps biological researchers analyze spatial transcriptomics data to understand complex biological processes, particularly in contexts like tumor microenvironments. You input raw spatial transcriptomics data, which includes gene expression and tissue morphology. It processes this information to produce interpretable, low-dimensional embeddings that reveal patterns in cell-to-cell interactions and tissue organization. This tool is for biologists, geneticists, and cancer researchers working with high-resolution tissue data.
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Use this if you need to deeply understand cellular interactions and tissue organization from spatial transcriptomics data, especially if interpretability of results is crucial for your research.
Not ideal if you are not working with spatial transcriptomics data or if you need a solution for a purely gene expression-based analysis without considering morphological features.
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29
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5
Language
Python
License
MIT
Category
Last pushed
Feb 16, 2024
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