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).
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: