Generating Cellular Spatial Organization with Denoising Papangelou Score Matching

Biological tissues are organized not only by which cell types are present, but also by how they form cellular neighborhoods, compartments, and interfaces. Generative models of spatial tissues should therefore capture not only cell counts and composition, but also spatial organization. We introduce Denoising Papangelou Score Matching (DPSM), a framework for generating cellular spatial configurations consisting of cell locations and types. DPSM uses time dependent Papangelou fields to model where cells of different types are likely to be inserted given the current configuration. Learned along a corruption path to a Poisson reference, these fields determine the reverse generative processes. We show that noisy pseudolikelihood performs population field matching, derive a denoising identity for the corrupted fields, and relate field estimation error to reverse generation error. Experiments on controlled interacting point processes, synthetic cellular patterns, and real world spatial omics data show that DPSM recovers local cell type associations and spatial organization while preserving cell counts and composition.