A diverse array of spatial learning methods tailored for analyzing spatially resolved single-cell multiplex omics data are introduced. Initially, SpatialViz pipelines are introduced, which serve as foundational tools for analyzing disease and health dynamics at the single-cell level within their spatial contexts. By expanding the repertoire of protein targets, SpatialVizScore and SpatialVizPheno enable the uncovering of multiple interaction types across patient tissues. Subsequently, the integration of metabolomic and protein data is presented as crucial for understanding metabolomic changes at the single-cell level, offering insights into cellular metabolism and its implications for disease pathogenesis and treatment response. As the adoption of spatial omics technologies increases across diseases, integrating multi-omics data becomes imperative for deciphering underlying biological processes. Lastly, graph-based deep learning pipelines are described for analyzing single-cell and subcellular omics data, offering a powerful framework for capturing complex relationships within spatially resolved datasets. These pipelines demonstrate the potential to extract disease biomarkers and predict treatment responses accurately and help with cases of missing or lower-quality multiplex stains.