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). DeePhys 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
• Dissect heterogeneous cell populations/cultures on the single-cell level

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.

The package is ready-to-use right after cloning. As the download via git-lfs is heavily limited, please download the spike sorted data for the tutorial here and replace the folder SortingExamples with the unzipped version from there. Preprocessed data can be downloaded here.

Code requires spikesorted data in the phy format. For help with spikesorting check out the Spikeinterface package.

The analysis pipeline is subdivided into the following modules (links to the tutorials):

• Feature Extraction
• Phenotype Generation
• Treatment Evaluation
• Single Cell Analysis

The DeePhys package was first published on bioRxiv, but was since heavily updated and is no longer compatible to the prior version.

This package uses several packages/toolboxes:

• the readNPY function provided by the npy-matlab package
• the CCG function provided by the FMAToolbox
• the othercolor function.
• the catch22 toolbox as published here
• the ISIN burst detection algorithm as published here
• the Brain Connectivity Toolbox