This is a project repository for -
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- Subedi, Sishir, and Yongjin P. Park. "Decomposing patient heterogeneity of single-cell cancer data by cross-attention neural networks." medRxiv 2025.06.04.25328900
Tutorial
For the step-by-step tutorial, please refer to notebooks :
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Tutorial 1. Training PICASA model using simulated datasets.
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Tutorial 2. Plotting all three latent representations learned by the model.
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Tutorial 3. Analysis of the cross attention matrix estimated by the model.
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Tutorial 4. Cancer common representation analysis.
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Tutorial 5. Cancer unique representation analysis.
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Tutorial 6. Cancer patient outcome analysis.
Metadata
Release files for picasa 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| picasa-0.1.0.tar.gz | 20.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| picasa-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 41.8 kB
Release files / picasa-0.1.0.tar.gz
| Download URL | picasa-0.1.0.tar.gz |
|---|---|
| Size | 20.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/6.1.0 CPython/3.11.11
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Release files / picasa-0.1.0-py3-none-any.whl
| Download URL | picasa-0.1.0-py3-none-any.whl |
|---|---|
| Size | 21.7 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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No |
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twine/6.1.0 CPython/3.11.11
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