Machine learning challenges in spatial single cell omics analysis

Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. The resulting large-scale and complex multimodal data sets raise interesting computational and machine learning challenges, from QC & storage to actual analysis. For instance what is the best description of a local cell neighborhood, how do we find such interesting ones and how are these differential across disease or other perturbations? And how can they be chained together to build a spatial human cell atlas?

Here, I will present approaches from the lab touching upon a few of these points. In particular, I will show how our recent toolbox Squidpy and the related SpatialData format support standard steps in analysis and visualization of spatial molecular data. I will then discuss recent approaches towards multimodal classification, learning cell cell communication and extension towards morphometric representations under perturbations using generative models.