autoMEA

autoMEA (Automated analysis of MEA datasets) is a open-source Python package for the analysis of Micro-Electrode Array (MEA) datasets.

How does autoMEA work?

Bursts are detected using the Max Interval Method. Users can manually set Max Interval Parameters, or can use a machine learning model that dynamically predicts optimal parameters for specific recording times. Several models are distributed with automea, and users are free to fine-tune the existing models for their specific needs, or upload new models completely.

The machine-learning-based burst detection routine is explained in the paper accompanying the package.

Tutorials and documentation can be found on readthedocs.

Installation

The preferred way to install autoMEA is using Conda.

Create a new environment with Python 3.10 and activate it:

conda create -n automea_env python=3.10
conda activate automea_env

Install autoMEA using pip:

pip install automea

Reproducibility

All the data used to train and evaluate the machine learning models distributed with autoMEA can be found on zenodo.

Citing

If you have used autoMEA for work that has led to a scientific publication, please cite it as

@article {Hernandes2024.05.08.593078,
	author = {Hernandes, Vinicius and Heuvelmans, Anouk M. and Gualtieri, Valentina and Meijer, Dimphna H. and van Woerden, Geeske M. and Greplova, Eliska},
	title = {autoMEA: Machine learning-based burst detection for multi-electrode array datasets},
	elocation-id = {2024.05.08.593078},
	year = {2024},
	doi = {10.1101/2024.05.08.593078},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/05/08/2024.05.08.593078},
	journal = {bioRxiv}
}

@dataset{hernandes_2024_12685150,
  author       = {Hernandes, Vinicius and
                  Heuvelmans, Anouk M. and
                  Gualtieri, Valentina and
                  Meijer, Dimphna H. and
                  van Woerden, Geeske M. and
                  Greplova, Eliska},
  title        = {{Data and scripts used in: "autoMEA: Machine 
                   learning-based burst detection for multi-electrode
                   array datasets"}},
  month        = jul,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.1101/2024.05.08.593078},
  url          = {https://doi.org/10.1101/2024.05.08.593078}
}

Authors

Here is a list of authors who have contributed to this project:

• Vinicius Hernandes
• Anouk M. Heuvelmans
• Valentina Gualtieri
• Dimphna H. Meijer
• Geeske M. van Woerden
• Eliska Greplova

Contributing

autoMEA is an open source package, and we invite you to contribute! You contribute by opening issues, fixing them, and spreading the word about autoMEA.

License

This work is licensed under a MIT License