blech_clust¶

Python and R based code for clustering and sorting electrophysiology data

Overview¶

blech_clust is a comprehensive Python and R based toolkit for clustering and sorting electrophysiology data recorded using the Intan RHD2132 chips. Originally written for cortical multi-electrode recordings in Don Katz's lab at Brandeis University, it's optimized for high-performance computing clusters but can be easily modified to work in any parallel environment.

Visit the Katz lab website for more information.

Key Features¶

Automated Spike Sorting: Complete pipeline from raw data to sorted units
EMG Analysis: Multiple approaches including BSA/STFT and QDA-based gape detection
Quality Assessment: Built-in tools for dataset quality grading and validation
Parallel Processing: Optimized for HPC environments
Comprehensive Documentation: Detailed API reference and tutorials

Quick Links¶

Getting Started - Installation and setup instructions
API Reference - Complete API documentation
Tutorials - Step-by-step guides
GitHub Repository

Pipeline Overview¶

Spike Sorting Pipeline¶

For the complete main spike-sorting pipeline (including the operations workflow diagram, detailed steps, and nomnoml schema), please refer to the README.

EMG Analysis Pipelines¶

For details on EMG analysis workflows, see the README and Workflow Documentation.

Installation¶

The installation process is managed through a Makefile that handles all dependencies:

# Clone the repository
git clone https://github.com/katzlabbrandeis/blech_clust.git
cd blech_clust

# Install everything
make all

# Activate the environment
conda activate blech_clust

For more detailed installation instructions, see the Getting Started guide.

Resources¶

blech_clust Secrets Within (PDF) - Brief guide to blech_clust internals and advanced usage
Auto Sorting Resources - Comprehensive documentation for the implemented auto sorting solution, including ML techniques and implementation details

Contributing¶

We welcome contributions! Please read our CONTRIBUTING.md file for guidelines.

Contributing to Documentation¶

Help us improve the documentation:

Report issues: Found an error or unclear explanation? Open an issue
Suggest improvements: Have ideas for better organization or content? We'd love to hear them
Submit changes: See docs/README.md for instructions on building and updating documentation

The documentation is built with MkDocs and automatically deployed via GitHub Actions.

Citation¶

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

@software{blech_clust_katz,
  author       = {Mahmood, Abuzar and
                  Mukherjee, Narendra and
                  Stone, Bradly and
                  Raymond, Martin and
                  Germaine, Hannah and
                  Lin, Jian-You and
                  Mazzio, Christina and
                  Katz, Donald},
  title        = {katzlabbrandeis/blech\_clust: v1.1.0},
  month        = apr,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {1.1.0},
  doi          = {10.5281/zenodo.15175273},
  url          = {https://doi.org/10.5281/zenodo.15175273}
}

Acknowledgments¶

This work used ACCESS-allocated resources at Brandeis University through allocation BIO230103 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by U.S. National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.

The project titled "Computational Processing and Modeling of Neural Ensembles in Identifying the Nonlinear Dynamics of Taste Perception" was led by PI Abuzar Mahmood. The computational allocation was active from 2023-06-26 to 2024-06-25.