Deep generative models for spatial reconstruction of single cell transcriptomics data

The spatial organization of cells underlies and influences the function of many biological systems, including tumors. Emerging biotechnologies now allow for spatially resolved quantification of transcriptomes at single cell resolution, but such technologies are expensive and relatively low-throughput. On the other hand, conventional single cell or single nucleus RNA-sequencing based technologies are now routinely applied to obtain genome-wide transcriptome profiles in hundreds of thousands to millions of cells, but do not retain spatial information.

The aim of this project is to employ deep generative models to reconstruct the spatial structure of biological samples from single cell transcriptomic count data alone. If successful, this project will allow for augmentation of single cell transcriptomics dataset with meaningful spatial information (e.g. extent of immune infiltration in tumor), adding great value to such datasets.