PACE, Proximity-Associated Changes in Expression

Abstract

Cellular transcriptional states are shaped by local tissue context, yet quantifying how cellular gene expression varies with proximity to different cell types remains challenging. Cell-resolved spatial transcriptomics data are typically sparse and susceptible to contamination from neighbouring cells through diffusion, imperfect segmentation and cell overlap, making it difficult to distinguish genuine cell-state changes from technical artefacts. We present PACE (Proximity-Associated Changes in Expression), a hierarchical empirical Bayes framework for quantifying cell-type-resolved proximity effects on gene expression. PACE uses partial pooling to stabilise inference across genes and cell types, separates contamination from biologically meaningful spatial associations, and identifies coordinated transcriptional programs underlying each proximity effect. Applied to Xenium-profiled breast cancer tissue, PACE reveals tumour-associated reprogramming of stromal cells and macrophages at tumour interfaces. In CosMx-profiled melanoma, it identifies fibroblast responses to tumour proximity, including extracellular matrix programs that differ between tumours from patients with progressive and stable disease following immunotherapy. PACE provides a robust and interpretable framework for quantifying how tissue organisation shapes cellular state in spatial molecular data.