Multiscale topology classifies and quantifies cell types in subcellular spatial transcriptomics

Spatial transcriptomics technologies produce gene expression measurements at millions of locations within a tissue sample. An open problem in this area is the inference of spatial information about single cells. Here we present a multiscale method to pinpoint the locations of individual sparsely dispersed cells from subcellular spatial transcriptomics data. We integrate this approach with multiparameter persistence landscapes, a state of the art tool in topological data analysis, to identify a loop structure in infiltrating glomerular immune cells in a mouse model of lupus nephritis. Joint work with colleagues in the Mathematical Institute and Wellcome Centre for Human Genetics at the University of Oxford, and the Beijing Genomics Institute.