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Multi-omics Autoencoder Integration: Deep learning-based heterogenous data analysis toolkit
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maui

maui

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Multi-omics Autoencoder Integration (maui) is a python package for multi-omics data analysis. It is based on a bayesian latent factor model, with inference done using artificial neural networks. For details, check out our pre-print on bioRxiv: https://www.biorxiv.org/content/early/2018/11/12/464743

Installation

maui works with Python 3.6 and TensorFlow 1.1 (does not yet support the yet unreleased TensorFlow 2.0). The easiest way to install is from pypi:

pip install -U maui-tools

This will install all necessary dependencies including keras an tensorflow. The default tensorflow (cpu) will be installed. If tensorflow GPU is needed, please install it prior to installation of maui.

The development version may be installed by cloning this repo and running python setup.py install, or, using pip directly from github:

pip install -e git+https://github.com/BIMSBbioinfo/maui.git#egg=maui

Optional dependencies

Survival analysis functionality supplied by lifelines 1. It may be installed directly from pip using pip install lifelines.

Usage

See the vignette, and check out the documentation.

Citation

Evaluation of colorectal cancer subtypes and cell lines using deep learning. Jonathan Ronen, Sikander Hayat, Altuna Akalin. bioRxiv 464743; doi: https://doi.org/10.1101/464743

Contributing

Open an issue, send us a pull request, or shoot us an e-mail.

License

maui is released under the GNU General Public License v3.0 or later.


@jonathanronen, BIMSBbioinfo, 2018

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