Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade
Alex C Soupir1, Ishaan V Gadiyar2, Bryan R Helm2
1Department of Biostatistics & Bioinformatics, Moffitt Cancer Center.
Insights
This study evaluated spatial co-localization metrics for single-cell multiplex imaging (scMI). Ripley's K and pair correlation g showed the most power for detecting immune cell co-localization and its association with patient survival in cancer studies.
Area of Science:
- Computational pathology
- Spatial biology
- Cancer research
Background:
- Single-cell multiplex imaging (scMI) reveals cell phenotypes and locations within tissues, crucial for understanding the tumor microenvironment.
- Quantifying immune cell spatial co-localization in scMI is vital for linking tumor microenvironment characteristics to clinical outcomes, yet optimal spatial indices remain unclear.
Purpose of the Study:
- To evaluate the performance of six frequentist spatial co-localization metrics in scMI data.
- To determine which spatial indices possess adequate power to detect within-sample co-localization and its association with patient survival.
Main Methods:
- Simulated scMI data were used to assess the power and type I error of six spatial co-localization metrics.
- The evaluated metrics were applied to scMI datasets from high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC) studies.
Main Results:
- In simulations, Ripley's K and pair correlation g demonstrated the highest power for detecting co-localization, outperforming other metrics.
- Analysis of cancer datasets confirmed that pair correlation g and Ripley's K were most effective for identifying significant co-localization.
- Pair correlation g, Ripley's K, and the scLMM index showed the greatest sensitivity in associating co-localization levels with patient survival differences.
Conclusions:
- Ripley's K and pair correlation g are powerful indices for detecting spatial co-localization in scMI data.
- These metrics, along with scLMM, are valuable for investigating the relationship between immune cell spatial organization and patient survival in cancer research.
Abstract:
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 signficant 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.
