Vasc-AoP: Data & Analyses

Data and R code for the Vasc-AoP project and paper

3D-image analysis and RT-qPCR data for the Vasc-AoP project were analysed in R, hosted on GitHub, and archived through Zenodo.

Bioinformatics

Biostatistics

Immunohistochemistry

Transcriptomics

RT-qPCR

Imaging

Description

The Vasc-AoP project included 3D-image analysis of cerebellar vascularization and RT-qPCR data, which were analysed in R (R Core Team, 2023). Data were modeled through the Generalized Linear Mixed Model (GLMM) framework, using the glmmTMB (Brooks et al., 2017) package. Random intercepts were added to account for the correlation between pseudo-replicates.

The optimal likelihood families were selected based on our theoretical understanding of the variable’s properties, and to minimize Aikake’s Information Criterion (AIC). Count data (e.g., branchpoints) were modeled using Negative Binomial likelihood, and measures bound at 0 (e.g., vascular volume) were modeled using a Gamma likelihood.

Model diagnostics were done 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 an innovative 3D imaging workflow combining IMARIS and VesselVio software to quantitatively analyze cerebellar vascularization at different postnatal stages. Transcriptomic analysis of 23 angiogenesis-related genes was performed to uncover associated molecular pathways.

Project (Vasc-AoP) Code Details

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
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/