Sikkim Mouse (Mus pahari) vs. C57BL/6J

This repository contains the data and analysis code for the bioRxiv preprint:

Vogt, C.C. and Sheehan, M.J. (2025) "The Sikkim mouse (Mus pahari) exhibits distinct spatial, circadian, and social behaviors compared to laboratory mice." bioRxiv, p. 2025.11.13.688123. Available at: https://doi.org/10.1101/2025.11.13.688123.

Citation

Anyone who uses this database or code should cite the above preprint until the manuscript is published elsewhere.

Repository Structure

├── data/
│   └── rfid/
│       ├── ALLTRIAL_RFID_PAHvsC57.part001.csv
│       ├── ALLTRIAL_RFID_PAHvsC57.part002.csv
│       ├── ALLTRIAL_RFID_PAHvsC57.part003.csv
│       ├── ALLTRIAL_RFID_PAHvsC57.part004.csv
│       ├── GBI_concat.csv
│       ├── GBI_summary.csv
│       └── metadata_PAHvsC57.xlsx
└── mainanalyses/
    ├── 2025_PAHvsC57_MainAnalyses.Rmd
    └── 2025_PAHvsC57_MainAnalyses.nb.html

Description

This repository provides behavioral data comparing Sikkim mice (Mus pahari, PAH) and C57BL/6J laboratory mice (C57) collected using RFID tracking in an indoor mesocosm environment. The study examines spatial behavior, circadian activity patterns, and social interactions between these two species.

Data Files

RFID Data

• ALLTRIAL_RFID_PAHvsC57.part001-004.csv: Split files containing the complete RFID tracking data for all trials. These files include timestamped location data with millisecond precision for individual mice tracked throughout the experimental period.

Social Network Data

• GBI_summary.csv: Individual-level summary of group-by-individual (GBI) data describing flocking behavior.

• GBI_concat.csv: Concatenated group-by-individual data across all trials.

Analysis Code

• 2025_PAHvsC57_MainAnalyses.Rmd: R Markdown notebook containing all statistical analyses, data processing, and figure generation for the manuscript.

• 2025_PAHvsC57_MainAnalyses.nb.html: HTML output of the R Markdown notebook for easy viewing of results.

Dependencies

The analysis code requires R with the following packages:

• fs, lubridate, knitr
• gganimate, gifski, av
• readxl, data.table
• ggplot2
• glmmTMB, DHARMa, lme4, lmerTest
• tidyr, dplyr
• lomb

Usage

1. Clone this repository
2. Ensure all required R packages are installed
3. Open mainAnalyses/2025_PAHvsC57_MainAnalyses.Rmd in RStudio
4. Update the working directory path in the setup chunk if needed
5. Run the analysis chunks sequentially

Contact

For questions about the data or analyses, please contact:

• Caleb C. Vogt, PhD

License

Please cite the preprint (DOI: 10.1101/2025.11.13.688123) when using this data or code.