Spatial Graphical Regression Models for Spatial Transcriptomics Data

Music City Center 

Modern spatial transcriptomic profiling techniques facilitate spatially resolved, high-dimensional assessment of cellular gene transcription across the tumor domain. The characterization of spatially varying gene networks enables the discovery of heterogeneous regulatory patterns and biological mechanisms underlying cancer etiology. We propose a spatial Graphical Regression (sGR) model to infer spatially varying graphs for high-resolution multivariate spatial data. Unlike existing graphical models, sGR explicitly incorporates spatial information to infer non-linear conditional dependencies through Gaussian processes. It conducts sparse estimation and selection of spatially varying edges, at both spatial and sub-spatial levels. Extensive simulation studies illustrate the profitability of sGR for spatial graph structural recovery and estimation accuracy. Our methods are motivated by and applied to two spatial transcriptomics data sets in breast and prostate cancer, to investigate spatially varying gene connectivity patterns across the tumor microenvironment.

spatial graphical regression, biological network, spatial transcriptomics, graphical model.