Single-Cell Electrophysiology Analysis & Clustering

This repository contains the analysis code, Jupyter notebooks, and extracted feature files accompanying the publication:

Joshi N, van Der Burg S, Celikel T, Zeldenrust F (2025) Neuronal identity is not static: An input-driven perspective. PLoS Comput Biol 21(12): e1013821. https://doi.org/10.1371/journal.pcbi.1013821

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Overview

This project provides the computational workflows for analyzing single-cell electrophysiological recordings and reproduces the results presented in the associated paper.

Key methods and analyses:

• Feature extraction from electrophysiological recordings
• Model fitting using GLIF models (see https://github.com/Nishant-codex/GIFFittingToolbox/tree/master)
• Clustering of single-cell responses across experimental conditions (ACSF vs. drug)
• Dimensionality reduction: PCA, UMAP, and probabilistic CCA (pCCA)
• Manifold alignment for comparing cell populations
• MCFA for comparing extracted features for heterogeneity
• Exploratory analyses of cluster stability, spike train metrics, and impedance profiles

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Repository Structure

notebooks_acsf/        # Analysis under ACSF conditions
notebooks_drug/        # Analysis under drug conditions
residual_code/         # Exploratory / legacy notebooks and scripts
│   └── Infomation_transfer/   # Cluster analysis, PCA, UMAP, stability checks
LICENSE
README.md              # Project description and usage

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Installation

Clone the repository and install dependencies:

git clone https://github.com/your-username/single_cell_analysis-clustering.git
cd single_cell_analysis-clustering
pip install -r requirements.txt

Alternatively, create a conda environment:

conda env create -f environment.yml
conda activate singlecell

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Usage

To reproduce the analyses, open the Jupyter notebooks:

jupyter notebook notebooks_acsf/Clustering_attribute_sets.ipynb

• notebooks_acsf/ → Main analysis for ACSF
• notebooks_drug/ → Drug condition analysis
• residual_code/ → Supplemental and exploratory analyses

Figures and results generated from these notebooks correspond directly to the analyses presented in the paper.

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Workflow

Below is a simplified workflow of the analysis pipeline:

flowchart TD
    A[Raw Electrophysiology Data] --> B[Feature Extraction]
    B --> D[Clustering & Manifold Alignment]
	D --> E[Multi-set correlation and Factor Analysis]
    E --> F[Comparative Analysis: ACSF vs Drug]
    F --> G[Figures & Results]

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Citation

If you use this code or workflows in your research, please cite:

@article{joshi2025neuronal,
  title={Neuronal identity is not static: An input-driven perspective},
  author={Joshi, Nishant and van Der Burg, Sven and Celikel, Tansu and Zeldenrust, Fleur},
  journal={PLOS Computational Biology},
  volume={21},
  number={12},
  pages={e1013821},
  year={2025},
  publisher={Public Library of Science San Francisco, CA USA}
}

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License

This project is licensed under the MIT License.