spyglass-paper

Code and materials for reproducing Figure 5 from the Spyglass paper, demonstrating neural decoding analysis pipelines using the Spyglass data analysis framework.

Citation

If you use this code, please cite:

Lee, K.H.*, Denovellis, E.L.*, Ly, R., Magland, J., Soules, J., Comrie, A.E., Gramling, D.P., Guidera, J.A., Nevers, R., Adenekan, P., Brozdowski, C., Bray, S., Monroe, E., Bak, J.H., Coulter, M.E., Sun, X., Broyles, E., Shin, D., Chiang, S., Holobetz, C., Tritt, A., Rübel, O., Nguyen, T., Yatsenko, D., Chu, J., Kemere, C., Garcia, S., Buccino, A., Frank, L.M., 2024. Spyglass: a data analysis framework for reproducible and shareable neuroscience research. bioRxiv. 10.1101/2024.01.25.577295.

* Equal contribution

Overview

This repository contains:

• Figure 5 generation scripts: Publication-quality figures showing neural decoding results from two datasets
• Jupyter notebooks: Example decoding analysis workflows using Spyglass
• Plotting utilities: Shared functions for consistent figure styling

Datasets

The figures demonstrate two decoding approaches:

1. Frank Lab (j1620210710): Clusterless decoding analysis
2. Buzsaki Lab (MS2220180629): Sorted spikes decoding analysis

Installation

Prerequisites

• Miniconda or Mamba
• A configured Spyglass database (see Spyglass documentation)

Setup

1. Clone the repository:

   git clone https://github.com/edeno/spyglass-paper.git
   cd spyglass-paper

2. Create and activate the conda environment:

   conda env create -f environment.yml
   conda activate spyglass-paper

For exact reproducibility of published figures, use the pinned environment:

conda env create -f pinned_paper_environment.yml

Data

Buzsaki Lab Dataset (MS2220180629)

Download from DANDI (https://doi.org/10.48324/dandi.000059/0.230907.2101):

dandi download DANDI:000059/0.230907.2101
mkdir -p data/nwb/raw/MS2220180629.nwb
mv sub-MS22_ses-Peter-MS22-180629-110319-concat_desc-processed_behavior+ecephys.nwb data/nwb/raw/MS2220180629.nwb/MS2220180629.nwb

Frank Lab Dataset (j1620210710)

This dataset will be available on DANDI soon.

Reproducing Figures

Step 1: Run the decoding notebooks

First, execute the Jupyter notebooks to populate the Spyglass database with decoding results:

jupyter lab notebooks/figure5/

Run each notebook:

• MS2220180629_sorted_decode.ipynb (Buzsaki Lab data)
• j1620210710_clusterless_decode.ipynb (Frank Lab data)

Step 2: Generate figures

After the notebooks have populated the database:

# Generate main Figure 5
python notebooks/figure5/figure5.py

# Generate supplemental figure (pipeline flowchart)
python notebooks/figure5-supplemental/figure5_supp.py

Output files (PDF and PNG) will be saved in the respective notebook directories.

Repository Structure

spyglass-paper/
├── src/
│   └── paper_plotting.py      # Shared plotting utilities
├── notebooks/
│   ├── figure5/               # Main figure generation
│   │   ├── figure5.py         # Figure 5 script
│   │   └── *.ipynb            # Decoding analysis notebooks
│   └── figure5-supplemental/  # Supplemental figure
│       └── figure5_supp.py    # Pipeline flowchart script
├── data/nwb/raw/              # NWB data files (not tracked)
├── environment.yml            # Conda environment
└── pinned_paper_environment.yml  # Exact versions for reproducibility

License

This project is licensed under the MIT License - see the LICENSE file for details.