multimil 0.3.2

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

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:

• Classification with MIL
• Regression with MultiMIL

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

2. 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.