How to Read Cross Pair Correlation in R Without Fake Repulsion (Part 3)

The cross pair correlation function can mistakenly interpret missing tissue, such as a glial scar, as repulsion between different cell types. This problem is demonstrated with a simulation where a classical estimator incorrectly shows repulsion due to a central lesion, even when cell types are independent. To correct for this, an inhomogeneous estimator and a random labelling reference are used to account for non-uniform cell density. The analysis is performed in R using the `spatstat` and `brms` packages. This statistical approach is applied to data from animal models of ischemia to accurately separate true cellular interactions from artifacts caused by tissue loss.