Statistical tests and statistical learning for omics data
This advanced statistics course that will include feature selection in statistical tests, clustering and statistical learning. Successful completion will be awarded with 1 ECTS.
Learning objectives:
- Obtaining an overview of current computational systems biology approaches for omics data analysis
- Understanding the principles behind data pre-processing, quality control and statistical analysis of large-scale biological datasets
- Obtaining experience in higher-level omics analysis via pathway and network analyses
- Learning the basics of machine learning analyses for omics sample clustering and classification
Registration
Registration is closed.
Requirements
- Basic working knowledge of R
- Have a computer ready with a recent version of R (4.0 or later) and RStudio (1.2 or later) installed. Please have your computer set-up well before the start of the course.
Time plan
Monday (14.09.2019)
| Time | Topic |
|---|---|
| 9.15 | Coffee & virtual get together |
| 9.45 | Lecture |
| Welcome & introduction | |
| Data pre-processing, filtering & quality control | |
| Feature selection and hypothesis tests | |
| Statistical meta-analysis of omics data | |
| 12.30 | Lunch break |
| 13.30 | Practical tutorial |
| Introduction to the example datasets used for the project | |
| Software installations on student laptops, retrieving public biomedical datasets | |
| Hands-on data pre-processing & differential abundance analysis | |
| 17.00 | End |
Tuesday (15.09.2019)
| Time | Topic |
|---|---|
| 9.15 | Coffee & virtual get together |
| 9.45 | Lecture |
| Cellular pathway & network analysis | |
| Unsupervised machine learning analysis (clustering) | |
| Supervised machine learning analysis (prediction) | |
| 12.30 | Lunch break |
| 13.30 | Practical tutorial |
| Hands-on pathway & network analysis | |
| Hands-on machine learning analyses | |
| Conclusion & final discussion | |
| 17.00 | End |
Instructors
Enrico Glaab with Veronica Codoni, Armin Rauschenberger and Leon-Charles Tranchevent
Address
The course will be held online. Access will be available for registered participants only.
