The aim of the course is to provide a practical introduction to the analysis of “omics” data. Topics will range from data visualization/exploration to univariate/multivariate analysis and machine learning. Practical examples and applications will be illustrated by using R and Python.

Course Milestones:

• Data exploration and visualization
• Univariate/Multivariate analysis
• Introduction to machine learning: classifiers, performance measures, diagnostics
• Machine learning tools for the analysis of Gene Expression data
• The Data Analysis Plan (DAP) – intro to unbiased pipelines for (binary) classification
• Performance measures and diagnostic plots – Accuracy, MCC, Stability: theory and graphics
• Differential network analysis – co-expression networks, graph comparison, community detection: theory and examples in R/Python, visualization by the igraph library and use of the ReNette web interface
• Basic application of ML to gene prediction