A written report is to be submitted after the course. Performance during the course will also be integrated into the final grade.

The basics of processing and automatically interpreting single-cell datasets are conveyed and applied on concrete examples in this practical course. The course tasks will be implemented in the scripting language Python and R. Specifically this course covers preprocessing, quality control of single-cell transcriptomic data and reconstruction of dynamic processes via RNA velocity analyses, trajectory inference and different dynamic models.

(1)The students learn how to perform quality control and normalization of single-cell RNA sequencing data.
(2) They learn how to visualize high dimensional single-cell data.
(3) They learn how to perform RNA velocity analyses.
(4) They learn how to establish dynamic models from RNA velocity fields.

Will be supplied during the course.