Course Overview

In this teaching unit, students will learn to understand, manage and
analyze high dimensional “omics” data, such as gene polymorphism
or gene expression (transcriptomic) data. Topics range from principles
of experimental clinical trials with high dimensional omics data in
small sample sizes to methods of gene-wise association analyses(GWAS)
in large observational studies. By the use of case studies and
hands-on computer programming to analyze real data during this unit,
the students will acquire knowledge in dimension reduction methods,
multiple testing procedures and longitudinal data modelling. Together
with annotation visualization techniques, this course will also teach
how to interpret and communicate results of omics analyses.

Learning Achievement

Competence

Course prerequisites

- General statistical theory concepts
- Programming / Exploratory Data Analysis / Getting
and Cleaning Data /  Statistical Inference / Regression Models
/ Statistical Inference
- Fundamentals of epidemiology

Grading Philosophy

- Continuous assessment (group presentations)

Course schedule

-
Principles of clinical trials;

-
Data management and data warehouse systems;

-
Genomics data: generation, management and analysis;

-
Gene and gene set annotations;

-
Descriptive analysis tools for high dimensional data;

-
Test multiplicity;

-
Principles of GWAS;

-
Predictive analyses;

- Mixed models

Course type

Lectures, practicals, inversed classes, project

Online Course Requirement

Instructor

Other information

Part of the Public Health Data Science Master programDuration: 3 weeks (63 hours)

Language of instruction: English
Mode of delivery: Blended-learning (63h in total)

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