mubind

Model highlights

• Mubind is a machine learning method for learning motif associations with single cell genomics data, using graph representations such as a k-nearest neighbors graph (kNN).
• It uses sample-sample operation (graphs) to communicate filter activities (learned motifs) across cells.
• The whole codebase is written in PyTorch.
• This package works with single-cell genomics data, scATAC-seq, scChIP-seq, etc. We have also tested it on bulk in vitro samples (HT-SELEX, PBM). Please see the documentation for related examples.

Workflow

Model architecture

Scalability

• Number of cells: The scalability of this method has been tested on single-cell datasets between 10,000 and 100,000 cells.
• Number of peaks: We have tested randomly selected features, or EpiScanpy's variability score. Modeling all features requires calibration of batch sizes and total GPU memory.
• Usual running times (one GPU): We get variable running times based on hyper-parameters, and they range between 10 minutes (prior filters) and 3 hours (de novo).

Resources

Please refer to the documentation.

• Tutorials

Installation

There are several alternative options to install mubind:

pip

1. Install the latest release of mubind from PyPI <https://pypi.org/project/mubind/>_:

pip install mubind

2. Install the latest development version:

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

conda

Available soon.

Release notes

See the changelog.

Contact

If you found a bug, please open an Issue.

Citation

If mubind is useful for your research, please consider citing as:

@software{mubind,
author = {Ibarra, Schneeberger, Erdogan, Martens, Aliee, Klein and Theis FJ},
doi = {},
month = {},
title = {{mubind}},
url = {https://github.com/theislab/mubind},
year = {2023}
}

Preprint

t.b.c.

Acknowledgments.

• Wellcome Leap | Delta Tissue
• Helmholtz Zentrum Muenchen.

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