Contents
Omics data provide deep insights into the molecular processes of life.
They present a challenge to data analysis in terms of their size, high-dimensionality and complexity.
Methods from topological data analysis have in recent years developed into powerful tools to tackle this problem.
This course offers an introduction to persistent homology and the Mapper algorithm, which are two of the most prominent methods in topological data analysis, and their applications to problems in omics data analysis.
We will cover the underlying mathematical theory, study concrete use cases in biology, and do some computer programming.
Biological applications will be based on genomic data from viral evolution and single-cell transcriptomic data from cancer biology.
Intended audience
Students and researchers in mathematics, data science, biology, medicine and related fields
Prerequisites
Very basic concepts from linear algebra, molecular biology and computer programming in Python.
No knowledge about topological data analysis or omics biology will be assumed.
Additional background information on mathematics, biology and computer programming will be provided in class depending on the audience's needs.
Time & place
Tuesday 16:15-17:45 (lectures) & Tuesday 14:15-15:45 (exercise classes)
Mathematikon INF 205, Seminarraum A & Zoom meeting (registration required)
Course format
This course is in hybrid format.
All sessions take place in-person, but remote participation is also possible via Zoom.
In-person participation is encouraged!
Registration
The course is open to participants from Heidelberg University, KIT, DKFZ, Heidelberg University Hospital and EMBL.
Please register to obtain more information and to be able to access the course material.
Participants from Heidelberg University can sign up in Moodle (enrollment key will be distributed in class).
All other participants please register by sending me an email from your institutional email address.