The implementation of the paper 'MetaQ: fast, scalable and accurate metacell inference via deep single-cell quantization'. Please refer to the paper and code repository (https://github.com/XLearning-SCU/MetaQ) for more details.
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Release files for MetaQ-sc 1.0.6
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| metaq_sc-1.0.6.tar.gz | 12.5 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| MetaQ_sc-1.0.6-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 45.5 kB
Release files / metaq_sc-1.0.6.tar.gz
| Download URL | metaq_sc-1.0.6.tar.gz |
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| Size | 12.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/6.0.1 CPython/3.13.0
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Release files / MetaQ_sc-1.0.6-py2.py3-none-any.whl
| Download URL | MetaQ_sc-1.0.6-py2.py3-none-any.whl |
|---|---|
| Size | 33.1 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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No |
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twine/6.0.1 CPython/3.13.0
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Security for MetaQ-sc 1.0.6
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