S3R: Spatially Smooth and Sparse Regression Reveals High-Dimensional Regulatory Networks in Spatial Transcriptomics

Abstract

Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general framework that estimates location-specific coefficients linking a response feature to high-dimensional spatial predictors. S3R unites structured sparsity with a minimum-spanning-tree-guided smoothness penalty, yielding coefficient fields that are coherent within neighborhoods yet permit sharp boundaries. S3R enables large scale data analysis with an efficient implementation using a reduced MST graph, multi-GPU training, and parallel hyperparameter search. In synthetic data, S3R accurately recovers spatially varying effects, selects relevant predictors, and preserves known boundaries. Applied to human dorsolateral prefrontal cortex, S3R recapitulates layer-specific target-TF associations with concordant layer-wise correlations in matched single-cell data. In acute Haemophilus ducreyi skin infection, S3R converts spot-level mixtures into cell type-attributed expression fields and reveals spatial gradients; applying SVG tests to these fields increases concordance and recovers gradients missed by spot-level methods. In pancreatic ductal adenocarcinoma, S3R constructs cross-cell type, cross-gene co-variation tensors that prioritize interactions among cell types with interacting genes enriching pathways consistent with known biology. Because responses and predictors in S3R are user-defined, it could flexibly address diverse ST questions within a single, scalable, and interpretable regression framework.