Note the 12:30 start time. Pizza lunch will be served before the talk, beginning at 12:15.

Spatial transcriptomics (ST) bring new dimensions to the analysis of single-cell data. Although some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing physical arrangement, where tissues are deformed in different ways at different time points. However, other challenges arise due to slicing bias and measurement granularity. I will examine the sources of these challenges, and explore how they are addressed with current state-of-the-art ST TI methods. I will conclude by highlighting some opportunities for future method development.