Weakly supervised learning uncovers phenotypic signatures in single-cell data

Weakly supervised learning uncovers phenotypic signatures in single-cell data#

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Getting started#

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and the tutorials:

Please also check out our sample prediction pipeline, which contains MultiMIL and several other baselines.

Installation#

You need to have Python 3.10 or newer installed on your system. We recommend installing Mambaforge.

To create and activate a new environment:

mamba create --name multimil python=3.10
mamba activate multimil

Next, there are several alternative options to install multimil:

  1. Install the latest release of multimil from PyPI:

pip install multimil

  1. Or install the latest development version:

pip install git+https://github.com/theislab/multimil.git@main

Release notes#

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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.

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