Teaching
MSc Public Health Data Science – ISPED, University of Bordeaux
Genomics Data: Generation, Management, and Analysis
Description: In this 6-hour course, I give an introduction to genomics data, focusing on the generation and analysis of transcriptomic data. In particular, I discuss:
- Quality control, filtering, normalisation, and batch effects
- Exploratory analyses: principal component analysis, hierarchical clustering algorithms
- Differential gene expression analysis
Note: Completing this course requires datasets which are transferred securely to the students in my classes.
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Genomics Data: Supervised Learning for High-Dimensional Data
Description: In this 6-hour course, I give an overview of supervised learning in the high-dimensional setting, focusing on regression tasks. I mainly discuss:
- Cross-validation for model evaluation, hyperparameter tuning
- Regularised methods (ridge, lasso, and elastic net regression)
- Latent variable approaches (principal component regression and partial least squares)
- Model interpretability
Note: Completing this course requires datasets which are transferred securely to the students in my classes.
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Practical work (questions) Download (.Rmd) Open in new tab (.html)
Practical work (solutions) Download (.Rmd) Open in new tab (.html)
Genomics Data: Test Multiplicity
Description: In this 3-hour course, I provide an overview of the multiple testing problem in high-dimensional settings. In particular, I discuss:
- An introduction to frequentist hypothesis testing
- Family-wise error rate and its control (Bonferroni, Holm)
- False discovery rate and its control (Benjamini-Hochberg, Benjamini-Yekutieli)
Note: Completing this course requires datasets which are transferred securely to the students in my classes.
Slides Download (PDF) Open in new tab
Practical work (questions) Download (.Rmd) Open in new tab (.html)
Practical work (solutions) Download (.Rmd) Open in new tab (.html)
Core Principles of Epidemiology
Description: In this 6-hour course, I give an introduction to epidemiology, discussing:
- Measures of association
- Study design
- Test diagnostics (sensitivity, specificity, PPV, NPV)
- Causality, bias, and directed acyclic graphs
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