AD-McGill: Data & Analyses

Data and R code for the AD-McGill project and paper

Immunohistochemical analyses of Bnip3 expression in an Alzheimer’s disease rat model were analyzed in R, hosted on GitHub, and archived through Zenodo.

Bioinformatics

Biostatistics

Immunohistochemistry

Imaging

Description

The AD-McGill project investigated Bnip3 expression patterns in the brain of a McGill-Thy1-APP transgenic rat model of Alzheimer’s disease. Immunohistochemical data were analyzed in R (R Core Team, 2023) to quantify Bnip3 optical density across different brain regions.

Optical density measurements were modeled through the Generalized Linear Mixed Model (GLMM) framework, using the glmmTMB (Brooks et al., 2017) package. Random intercepts for subjects were included to account for the correlation between measurements from the same animal. The optimal likelihood family was selected based on theoretical understanding and to minimize Aikake’s Information Criterion (AIC). Optical density data were modeled using a Gamma likelihood with a log-link function.

Model diagnostics were performed using the DHARMa (Hartig, 2022) & performance (Lüdecke et al., 2021) packages, and estimated marginal means/contrasts were computed with the emmeans package (Lenth, 2022).

The project employed quantitative immunohistochemistry to measure Bnip3 protein expression across multiple brain regions (CA1, CA2/3, subiculum, and entorhinal cortex) in both wild-type and transgenic rats, revealing region-specific and genotype-dependent expression patterns.

Code Details

Immunohistochemistry showing Bnip3 expression in the rat brain

Repository Structure

The analysis code and data are organized in the following structure:

analysis/: R Markdown files with the complete data analysis pipeline
data/: Raw and processed datasets containing optical density measurements
src/: Custom R functions developed for the project
fig/: Generated figures and visualizations
Uses renv for reproducible package management

References

Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400. https://journal.r-project.org/archive/2017/RJ-2017-066/index.html

Hartig, F. (2022). DHARMa: Residual diagnostics for hierarchical (multi-level / mixed) regression models. https://CRAN.R-project.org/package=DHARMa

Lenth, R. V. (2022). Emmeans: Estimated marginal means, aka least-squares means. https://CRAN.R-project.org/package=emmeans

Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). performance: An R package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/