DE-AoP

Developmental effects of Apnea of Prematurity

This project studies the underlying molecular and cellular mechanisms of apnea of prematurity at play during cerebellar development, using intermittent hypoxia in a mouse model.
R
Bioinformatics
Biostatistics
Software Engineering
Immunohistochemistry
Transcriptomics
RT-qPCR
Consortium
Began Around

August 1, 2019

Abstract

This work aims at shedding light on the mechanisms underlying cerebellar hypoxic injury. To this end, a transcriptomic study (by RT-qPCR) of genes involved in oxydative stress, cell differentiation, and migration was performed. We analyzed the expression of these genes in different developmental stages (P4, P8, P12, P21 and adults), and in different cell types, using laser capture microdissection to separate cerebellar layers. This project provides cues to better understand the cellular and molecular aspects of AoP-induced cerebellar injury.


Graphical abstract recapitulating the main findings of this project

Summary

The DE-AoP project included RT-qPCR and immunohistochemistry data, which were analysed in R (R Core Team, 2023). 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).

NoteMy role in this project

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

1) Developed tools to assist the project’s researchers in exploring their data. Among those tools, I coded and hosted a modular Shiny dashboard to assist in the data exploration process (R/Shiny).

3) Participated in writing the journal article summarizing the results of this project (Rodriguez-Duboc et al., 2023).

4) Open-sourced the resulting code on GitHub and registered it on Zenodo.

5) Made a website documenting and showcasing the project’s data, analyses, and results. The website uses Quarto and Javascript to provide an interactive dashboard to explore the data.

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
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/
Rodriguez-Duboc, A., Basille-Dugay, M., Debonne, A., Rivière, M.-A., Vaudry, D., & Burel, D. (2023). Apnea of prematurity induces short and long-term development-related transcriptional changes in the murine cerebellum. Current Research in Neurobiology, 5, 100113. https://doi.org/10.1016/j.crneur.2023.100113