LT-AoP

Long Term effects of Apnea of Prematurity

This project studied the impact of apnea of prematurity on cerebellar development and the long-term functional deficits resulting from it, using intermittent hypoxia in a mouse model.
R
Software Engineering
Bioinformatics
Biostatistics
Transcriptomics
RT-qPCR
Consortium
Began Around

August 1, 2016

Abstract

This study aimed to identify the effects of AoP on cerebellar development, and to demonstrate that the cerebellum is vulnerable to AoP. This was shown via enzymatic assay studies and RT-qPCR. Results confirmed the presence of alterations of cellular mechanisms, such as oxidative stress, leading to a delay in cerebellar maturation. This was further assessed through visible changes in cellular phenotype and histology via immunocytochemistry. Furthermore, behavioral studies showed that functional and behavioral alterations persisted in adulthood.


Summary

The LT-AoP project included behavioral, immunohistochemistry and RT-qPCR data, which were analysed in R, hosted on GitHub, and archived with Zenodo. Data were modeled through the Generalized Linear Mixed Model (GLMM) framework, using the glmmTMB package (Brooks et al., 2017). Random intercepts were added to account for the correlation between pseudo-replicates. Temporally-dependent repeated measures were modeled using auto-regressive (AR1) terms.

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., cell counts, number of maze entries, …) were modeled using a Generalized Poisson likelihood, measures bound at 0 (e.g., cell density, volumes, weights, …) were modeled using a Gamma likelihood, and proportions (e.g., ratios of areas) with a Beta 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).

A website documenting the analyses and their results was generated using Quarto, to allow interested readers to explore our data & models’ outputs without having to run the code themselves.

NoteMy role in this project

1) Handled the data processing and analysis, for both immunohistochemistry and RT-qPCR data (R/Markdown).

2) Helped write the methodology/statistics part of the paper (Leroux et al., 2022).

3) Open-source the code and registered on Zenodo.

4) Made a website documenting and showcasing the project’s data, analyses, and results. The website uses Quarto and relies on templates to automatically generates documentation for each of the ~70 variables analyzed during the project.

Quarto website

We made a Quarto website to showcase the project’s data, analyses, and results with interactive plots and tables:

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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
Leroux, S., Rodriguez-Duboc, A., Arabo, A., Basille-Dugay, M., Vaudry, D., & Burel, D. (2022). Intermittent hypoxia in a mouse model of apnea of prematurity leads to a retardation of cerebellar development and long-term functional deficits. Cell & Bioscience, 12(1), 148. https://doi.org/10.1186/s13578-022-00869-5
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