Welcome to DeePhys

The package for Deep electrophysiological phenotype characterization:

<img src="https://github.com/user-attachments/assets/dfa63a20-211d-43e7-85ff-fc12b16bb68d" alt="Analysis schematic" style="width:500px;"/>

Created with BioRender

Overview

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

Requirements

Currently DeePhys is only available on MATLAB, so a recent MATLAB installation (>2019b) is required.

Installation

The package is ready-to-use right after cloning.

Usage

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

We provide several tutorials and a dataset (to be added) from our most recent paper (to be published) to facilitate picking up the analysis workflow: The analysis pipeline can be subdivided into the Feature Extraction, which performs a basic quality control and extracts features from the spike-sorted data, and the Phenotype Generation that runs the core analyses. The BasicFeatureExtraction tutorial shows how to set up the feature extraction, while the FeatureInspection tutorial shows how to inspect the analysis results and adjust them if necessary. After that, you can follow the NetworkPhenotype tutorial or the SingleCellPhenotype tutorial, which showcase how to perform the analyses shown in the original DeePhys paper and our latest publication on it (currently in print).

The dataset of the original paper and the old tutorials are still available, however, there will probably be compatibility problems due to frequent updates. They might, however, still be useful to understand specific analysis workflows.

Citation

If you find this package helpful or used in your analyses, please cite the DeePhys paper and link to this github repository.

Dependencies

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

Issues?

If you face any problems or bugs, or have ideas for additions to this package please open an issue.