Weakly supervised learning uncovers phenotypic signatures in single-cell data
Getting started
Please refer to the documentation. In particular, the
and the tutorials:
Please also check out our sample prediction pipeline, which contains MultiMIL and several other baselines.
Installation
You need to have Python 3.12 or newer installed on your system. We recommend using uv for environment management.
Install the latest release of multimil from PyPI:
uv pip install multimil
Or install the latest development version:
uv pip install git+https://github.com/theislab/multimil.git@main
Alternatively, with plain pip:
pip install multimil
Release notes
See the changelog.
Contact
If you found a bug, please use the issue tracker.
Citation
Weakly supervised learning uncovers phenotypic signatures in single-cell data
Anastasia Litinetskaya, Soroor Hediyeh-zadeh, Amir Ali Moinfar, Mohammad Lotfollahi, Fabian J. Theis
bioRxiv 2024.07.29.605625; doi: https://doi.org/10.1101/2024.07.29.605625
Reproducibility
Code and notebooks to reproduce the results from the paper are available at theislab/multimil_reproducibility.
1 maintainer
Metadata
1 maintainer
Release files for multimil 1.0.0
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Source distribution (sdist)
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| multimil-1.0.0.tar.gz | 2.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| multimil-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.9 MB
Release files / multimil-1.0.0.tar.gz
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| Size | 2.9 MB |
| Tags | Source |
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| Tags | Python 3 |
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