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.
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):
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