Abstract Human cancers are highly heterogeneous, arising from genetic, epigenetic, genomic, and gene environment interactions. Studying single-cell cancer heterogeneity is essential for effective diagnosis, prognosis and development of personalized anti-cancer therapy. However, single-cell level tumor heterogeneity in situ with the original spatial context is not addressed until very recently, with development of new frontier technological platforms such as spatial transcriptomics (ST) and single cell imaging mass cytometry (IMC). Due to complexity of these new spatial data types, computation is a major bottle neck to bring these technologies to precision therapeutic interventions in the clinical space. In this project, we take a three-pronged approach to propose a series of novel computational methods that will harness the power of spatially informative omics and imaging data, for drug treatment and cancer patient prognosis predictions. Building upon a previously highly productive R01 project, we aim to continue investigations in single cell research, with a new focus on single-cell spatial data analysis. First, we will develop a novel personalized drug repurposing algorithm called STADS using cancer spatial transcriptomics data. Next, build a new computational model STimpute to impute spatial transcriptomics data from easily accessible histopathology image data, using transfer learning and graph neural network (GNN) models. STimpute will allow predictions of drugs from histopathology data, by using imputed ST data as the proxy input of STADS. Lastly, we will build a new computational framework scImageProg to predict patient survival at the population level from the single-cell image cytometry data, accomplishing multi-scale modeling to link single-cell data to population health. The work will be expected to have transformative clinical impacts from various cutting-edge spatially informative and complex genomics and imaging data types.