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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

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Statistics and Data Science in Imaging
|March 7, 2025
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Summary
This summary is machine-generated.

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.

Keywords:
co-clusteringmultiplex imagingspatial biologyspatial proteomics

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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.