Multiscale approaches for understanding single cell spatial omics data

In person only

Technological advances in measuring gene expression in a spatially resolved manner have resulted in several tour-de-force publicly available datasets, often accompanied by sample-matched dissociated single cell RNA-seq or single cell multi-omic measurements. However, methodologies for analyzing such data are in urgent need of development. Currently, many integrative data analysis tasks for spatial genomics are performed using tools designed with dissociated single cell RNA-seq data in mind, effectively ignoring the specific data structures of spatial genomics data. This talk will cover recent methodological developments in harnessing all available information from molecule-resolved spatial omics as well as existing scRNA-seq datasets to address questions in biological and understanding disease.