Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer

Single-cell multiplex imaging (scMI) measures cell locations and phenotypes within a tissue and can be used to understand the tumor microenvironment. In scMI studies, it is often of interest to quantify spatial co-localization of immune cells and its association with clinical outcomes; however, it remains unknown which of the many available spatial indices have adequate power to detect spatial within-sample co-localization and its association with patient outcomes, such as survival. In this study, the performance of six frequentist metrics of spatial co-localization used in scMI studies were evaluated. Simulated data was used to assess the power and Type I error of these spatial metrics to detect significant co-localization. Furthermore, these spatial co-localization methods were applied to two scMI studies—a high-grade serous ovarian cancer (HGSOC) study and triple negative breast cancer (TNBC) study—to detect within-sample co-localization between cell types and their sensitivity to detect differences in survival across samples. In the simulation study, Ripley’s K had the greatest power to identify co-localization followed closely by pair correlation g; all other statistics showed little power across all simulation scenarios. In the application of the methods to cancer studies, the results consistently point to pair correlation g and Ripley’s K as indices with the most power for detecting significant co-localization in scMI data. Furthermore, pair correlation g, Ripley’s K, and the scLMM index were most effective for estimating between-sample associations between level of co-localization and survival. Supplementary materials for this article are available online.