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.
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Documentation and guides to using and troubleshooting bombcell can be found on the dedicated wiki.
Below is a flowchart of how bombcell evaluates and classifies each unit:
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 bombcell_pipeline to see an example workflow and play around with our small toy dataset. You can also use the MATLAB live script gettingStarted
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\+helpersin matlab and runmex -O CCGHeart.cto able to compute fast ACGs, using part of the FMAToolbox.
- 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.
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
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.
If you run into any issues or if you have any suggestions, please raise a github issue or create a pull request. You can email us: juliemfabre[at]gmail[dot]com, but github issues are preferred. You can also use the Neuropixels slack workgroup. Please star the project to support us, using the top-right "⭐ Star" button.