Mining spatial -omics data with a spatially-aware high-dimensional regression method

Music City Center 

Dissecting spatially varying relationships among features such as cell type interactions, gene regulatory networks, or microenvironmental cues, requires regression models that explicitly address the dual challenges of spatial dependency and high dimensionality. Traditional spatial regression methods often fail to balance spatial smoothness with feature sparsity, leading to overfit models or loss of interpretability in complex biological systems. To address this, we introduce Spatially Smooth Sparse Regression (S3R), a framework designed to resolve spatially coherent feature relationships through a unified regularization approach. S3R integrates (1) graph-guided spatial smoothing using minimum spanning trees (MSTs) to encode tissue topology, (2) L1/L2 penalties for individual and group-level sparsity, and (3) Adam optimizer-driven gradient descent for scalable high-dimensional optimization.
Applied to spatial transcriptomics data, S3R outperforms existing methods in accuracy and interpretability, recovering ground-truth coefficient and sparse feature sets. In biological contexts, S3R dissects feature relationships critical to tissue organization: it identifies layer-specific transcription factors in the human brain, macrophage-driven inflammatory response in infected skin, and collagen-mediated T cell exclusion in breast cancer stroma. The model further resolves spatially restricted ligand-receptor pairs in pancreatic tumors invisible to single-cell analyses.
By rigorously addressing the spatial regression problem, S3R empowers researchers to unravel spatially organized regulatory networks across development and disease domains. The method's open-source implementation enables scalable, interpretable analysis of 10x Visium, Xenium, and MERFISH datasets, bridging a critical gap in spatial omics.