ISPAT3D - Spatial Conditional Association Networks in Registered Tumor Volumes

Fits tumor-zone-specific conditional cell-density networks from registered three-dimensional multiplex imaging. An anisotropic Matern-3/2 Gaussian process is estimated per variable and zone using a Vecchia likelihood on spatially balanced anchors; predictions at all selected cells yield residual covariance sufficient statistics. Gaussian maximum likelihood then fits a shared-plus-zone factor covariance model. A matched section-wise planar fit uses the same selected cells and covariance estimator. This extends the spatially informed cell-density analysis of Bhadury et al. (2026) <doi:10.1038/s41598-026-35341-8>.