Abstract
Spatial transcriptomics technologies such as Xenium, MERFISH, and Visium HD enable high-resolution profiling of gene expression while preserving tissue architecture. However, most computational methods for spatial analysis do not explicitly model local tissue context, such as boundaries, neighborhoods, or gradients. Here, we present SpNeigh (https://github.com/jinming-cheng/SpNeigh/), an R package for spatial neighborhood analysis and spatially aware differential expression modeling. SpNeigh includes tools for boundary detection, spatial neighborhood extraction, distance-based weighting, and gradient-based statistical testing. It supports both region-based differential expression and smooth spatial modeling using spline-based regression, along with a spatial enrichment index that identifies genes enriched near defined spatial features. We demonstrate the utility of SpNeigh across multiple platforms and tissues, including mouse brain, human breast cancer, and human liver, revealing intermediate populations at tissue interfaces, immune microenvironment differences, and spatially zonated gene expression patterns. SpNeigh offers a flexible and interpretable framework for dissecting spatial gene expression dynamics in complex tissues.