Density-adjusted analysis of cell interactions to decipher tissue landscape changes.

Cell-cell proximity influences tissue homeostasis and disease progression, yet robust quantification across varying cell abundances remains challenging. We introduce a Monte Carlo simulation framework using the G-function as a spatial randomness reference to detect proximity differences between case groups independent of cell count. Three metrics, G-area, G-difference, and G-ratio, were evaluated for summarizing G-function outputs, alongside established approaches such as the Morisita-Horn Index and likelihood ratio. G-area most accurately captured group-level proximity changes. To demonstrate generalizability, we validated G-area in two external multiplex imaging datasets from colorectal and prostate cancer. This framework provides a cell-count-robust method for spatial analysis, enabling more reliable detection of microenvironmental changes across diseases and imaging platforms.