Events

Key statistical concepts and methods including t-tests, linear and logistic regression, and power calculations with hands-on practice.

Analysis of large-scale omics datasets (metabolomics, proteomics, lipidomics, transcriptomics, spatial and imaging data) with hands-on experience in quality control, preprocessing, and analysis.

Principles and analysis of single cell transcriptomics, including data processing, analysis and interpretation of differential gene expression.

Genetic analysis using real-world data, covering approaches for
integrating large-scale genomic datasets with clinical and population-level data.