Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases#

Getting started#

Please refer to the documentation. In particular, the

API documentation

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:

Install the latest release of multimil from PyPI:

pip install multimil

Or install the latest development version:

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

Release notes#

See the changelog.

Contact#

If you found a bug, please use the issue tracker.

Citation#

Multimodal Weakly Supervised Learning to Identify Disease-Specific Changes in Single-Cell Atlases

Anastasia Litinetskaya, Maiia Shulman, Soroor Hediyeh-zadeh, Amir Ali Moinfar, Fabiola Curion, Artur Szalata, Alireza Omidi, Mohammad Lotfollahi, and Fabian J. Theis. 2024. bioRxiv. 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.

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

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