- Recap: Rstudio, R data structure, programming
- Visualisation (chap 3 MSMB)
- Advanced R data structures: object and Bioconductor data structures
- Data normalisation: centering, scaling, quantile normalisation
- Manipulating biologial sequences using Biostrings and application (finding motif in protein sequences).
- Introduction to statistical learning
- Hypothesis testing (t-test, linear regression, multiple testing, p-value distributions), empirical approximations of probabilites (see https://bearloga.github.io/approximating-probability/)
- Unsupervised learning and clustering
- Dimensionality reduction (see http://rpubs.com/Saskia/520216 for useful info)
- Supervised learning and classification
- Plotting omics data: GViz, volcano plots, MA plots, heatmaps,
- Conclusions
Other possible topics:
- evolution
- networks
- GSEA
- Seminars about several **databases/datasets of interest** (could also be given as part of the masters course)
- **DBMS and SQL**
- **terminal and ssh**