Introductory lecture on stats methods
Trainer: Britta Velten
Overview: An integrative analysis of multi-omics data is essential to leverage the power of modern omics technologies and obtain comprehensive insights into the molecular mechanisms underlying a biological process or disease. The heterogeneity and high-dimensionality of the data however often poses challenges for the data integration. This lecture will provide an overview of the basic statistical concepts that are important for the joint analysis of multi-modal data. This will explain statistical properties of multi-omics data and the resulting challenges for the data analysis, followed by providing an overview on different strategies for both supervised and unsupervised integration of multi-omics data. As important examples this will cover both the concepts of regression-based analyses to map associations across omics layers as well as matrix factorization and dimension reduction as a tool for the visualization and unsupervised integration of omics data.
Learning outcomes
By the end of this session you will be able to:
- Familiarise yourself with the statistical challenges and pitfalls in multi-omics integration
- Identify different integration strategies and discover how to determine the right strategy for your application
- Describe the statistical concepts underlying common integration methods
Materials: