ttgump/scDHMap
Model-based deep hyperbolic manifold learning for visualizing complex hierarchical structures in single-cell genomics data
This tool helps single-cell biologists and researchers visualize the complex hierarchical structures within single-cell genomic data. It takes your raw single-cell gene count matrices, optionally from multiple experimental batches, and provides a simplified 2D map of cell relationships and denoised gene counts. This is ideal for scientists trying to understand cell development, differentiation pathways, or the impact of treatments.
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Use this if you need to reduce the complexity of single-cell sequencing data to visualize cell trajectories, correct for experimental batch effects, or clean up noisy gene counts.
Not ideal if your primary goal is not visualizing hierarchical cell relationships or if you are not working with single-cell genomics data.
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8
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Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Jul 10, 2024
Commits (30d)
0
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