General data
| Credits (SWS) | 12 (10 SWS) |
| Module level | Master |
| Language | German/English |
| Total hours | 360 h |
| Weekly time slot | 2-3 per week, with in-person attendance in Weihenstephan on Tuesdays |
| Block part in semester break | 03.08 - 14.08 or 21.09 - 02.10 |
Time schedule of the internship
| Apr 2026 | Kickoff meeting and assignment of projects and teams |
| Apr - Aug 2026 | Division of the project work, interim presentations |
| Aug - Oct 2026 | Blockpart for finalizing the project work, writing the report, and preparing the final presentation |
Requirements and prior knowledge
Bachelor's degree in Bioinformatics. Good Python programming skills. Interest in data visualization and benchmarking of existing tools.
Spatial technologies provide opportunities to study cellular mechanisms in a tissue context. In single-cell data, trajectory inference aligns cells based on the pseudo-time that is driven by cell fate/differentiation or other dynamic process. In spatial data, several tools extend this idea by leveraging tissue coordinates and local neighborhood structure to infer spatially informed trajectories. However, these tools have not yet been comprehensively benchmarked across tissues, experimental platforms, and ground-truth settings. In this project, you will explore and run a benchmarking analysis for spatial trajectory inference, evaluate methods under various scenarios (e.g., varying resolution, noise, and sampling density), and derive practical guidance on which approaches work best for which tissue types and technologies.
References:
- stlearn https://www.nature.com/articles/s41467-023-43120-6
- SpaceFlow https://www.nature.com/articles/s41467-022-31739-w
- Single-cell trajectory benchmark https://www.nature.com/articles/s41587-019-0071-9