We rewrote the analysis pipeline, which can now be found as the proper DeePhys package This repository is no longer maintained and we encourage you to use the DeePhys repo instead
The package for Deep electrophysiological phenotype characterization:
Created with BioRender
DeePhys was created to facilitate the analysis of extracellular recordings of neuronal cultures using high-density microelectrode arrays (HD-MEAs). MADEB allows users to easily:
- Extract electrophysiological features from spikesorted HD-MEA recordings
- Visualize differential developmental trajectories
- Apply machine learning algorithms to classify different conditions
- Obtain biomarkers predictive of the respective condition
- Evaluate the effect of treatments
Currently DeePhys is only available on MATLAB, so a recent MATLAB installation (>2019b) is required. We plan on expanding DeePhys to Python in the near future.
If you want to use the Notebooks, you need to install Jupyter lab or Jupyter notebook (pip install jupyterlab or pip install notebook) and the MATLAB kernel (pip install matlab_kernel) + the MATLAB API for Python.
However, all analysis scripts are also available as MATLAB live scripts, which do not require any additional software.
The package is ready-to-use right after cloning.
Code requires spikesorted data in the phy format. For help with spikesorting check out the Spikeinterface package.
Different parts of the analysis are subdevided into different analysis scripts:
- Feature extraction (Jupyter notebook, MATLAB live script)
- Data exploration
- Classification analysis
- Treatment evaluation
This package was published in "Electrophysiological classification of iPSC-derived dopaminergic neurons harbouring the SNCA-A53T mutation" and additionally contains code to replicate the figures used in the publication.
This package uses the readNPY function provided by the npy-matlab package, the CCG function provided by the FMAToolbox, and the othercolor function.