- Cours (CM) 6h
- Cours intégrés (CI) -
- Travaux dirigés (TD) 4h
- Travaux pratiques (TP) -
- Travail étudiant (TE) -
Langue de l'enseignement : Français
Description du contenu de l'enseignement
Acquire basic skills to analyze High-dimensional omics data.
Compétences à acquérir
- Understand the different features of the three dimension reduction techniques : Principal Component Analysis (PCA), Multidimensional Scaling (MDS), Stochastic Neighbour Embedding (SNE)
- Know some methods of Classification of High-dimensional omics data: Bayes rule, linear and quadratic discriminant analysis
- Understand some methods of Clustering of High-dimensional omics data : K-means, Agglomerative clustering (dendogram)
Bibliographie, lectures recommandées
Susan Holmes, Wolfgang Huber. Modern Statistics for Modern Biology. http://web.stanford.edu/class/bios221/book/