💣 Bombcell: find bombshell cells! 💣

Manual curation of electrophysiology spike sorted units is slow, laborious, and hard to standardize and reproduce. Bombcell is a powerful toolbox that addresses this problem, evaluating the quality of recorded units and extracting essential electrophysiological properties. Bombcell can replace manual curation or can be used as a tool to aid manual curation. See this talk at the annual Neuropixels course about quality control.

📢 We now have a Python of version of bombcell! See the installation instructions below to get started! 📢

Please star the project to support us, using the top-right "⭐ Star" button.

📔 Bombcell wiki

Documentation and guides to using and troubleshooting bombcell can be found on the dedicated wiki.

🔍️ How bombcell works

Below is a flowchart of how bombcell evaluates and classifies each unit: <img align="center" src="./docs/images/bombcell_flowchart.png" width=100% height=100%>

🏁 Quick start guide

Overview

Bombcell extracts relevant quality metrics to categorize units into four categories: single somatic units, multi-units, noise units and non-somatic units.

Take a look at:

the MATLAB live script gettingStarted to see an example workflow and play around with our small toy dataset.
the Python Jupyter notebook BC_demo and software specific ones : BC_demo_spikeGLX , BC_demo_open_ephys and BC_demo_other_recording_software
You can also take a look at the exercise we prepared for the 2024 Neuropixels course here.

Installation

Matlab

Bombcell requires MATLAB>=2019a.

To begin using Bombcell:

clone the repository and the dependencies. You can do this either via git/GitHub desktop or directly by downloading the .zip file and decompressing it.
add bombcell's and the dependancies' folders to MATLAB's path.
in addition, if you want to compute ephys properties, change your working directory to bombcell\+bc\+ep\+helpers in matlab and run mex -O CCGHeart.c to able to compute fast ACGs, using part of the FMAToolbox.

Dependencies

npy-matlab, to load .npy data in.
If you have z-lib compressed ephys data, compressed with mtscomp, you will need the zmat toolbox. More information about compressing ephys data here.
prettify-matlab, to make plots pretty.
MATLAB toolboxes:
- Signal Processing Toolbox
- Image Processing Toolbox
- Statistics and Machine Learning Toolbox
- Parallel Computing Toolbox
- Optimization Toolbox

In addition we would like to acknowledge:

to compute fast ACGs, we use a function (CCGHeart.c) part of the FMAToolbox, and it is already included in bombcell.
to read in spikeGLX meta data, we use a function from Jennifer Colonell's SpikeGLX_Datafile_Tools repository.

Python

Latest stable version

# Create a conda environment
conda create -n bombcell python=3.11
conda activate bombcell
# Install bombcell
pip install uv
uv pip install bombcell # you could do `pip install .`, but uv is much quicker!

Dev version (with the latest updates):

# Create a conda environment
conda create -n bombcell python=3.11
conda activate bombcell
# Clone latest bombcell repository from github
git clone https://github.com/Julie-Fabre/bombcell.git
cd bombcell/py_bombcell
# Install bombcell
pip install uv
uv pip install -e .

🤗 Support and citing

If you find Bombcell useful in your work, we kindly request that you cite:

Julie M.J. Fabre, Enny H. van Beest, Andrew J. Peters, Matteo Carandini, & Kenneth D. Harris. (2023). Bombcell: automated curation and cell classification of spike-sorted electrophysiology data. Zenodo. https://doi.org/10.5281/zenodo.8172821

:page_facing_up: License

Bombcell is under the open-source copyleft GNU General Public License 3. You can run, study, share, and modify the software under the condition that you keep and do not modify the license.

📬 Contact us

If you run into any issues or if you have any suggestions, please raise a github issue or create a pull request. You can also use the Neuropixels slack workgroup. Please star the project to support us, using the top-right "⭐ Star" button.

Bombcell

Install / Use

README