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

Misha Siddiqui1,2, Azam Hamidinekoo3, Jennifer Y Tan4

  • 1Molecular Pathology, The Institute of Cancer Research (ICR), London, UK.

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|May 27, 2026
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Summary

Quantifying cell proximity in tissues is hard. A new Monte Carlo simulation framework using the G-function accurately detects proximity changes, independent of cell count, for robust spatial analysis in diseases.

Keywords:
cancercell biologycomputational bioinformatics

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Area of Science:

  • Spatial biology
  • Computational pathology
  • Biostatistics

Background:

  • Cell-cell proximity is crucial for tissue homeostasis and disease progression.
  • Existing methods for quantifying spatial relationships struggle with varying cell abundances.
  • Accurate spatial analysis is vital for understanding complex biological systems.

Purpose of the Study:

  • To develop a robust method for quantifying cell-cell proximity that is independent of cell count.
  • To introduce a novel Monte Carlo simulation framework utilizing the G-function for spatial randomness reference.
  • To evaluate the performance of new metrics (G-area, G-difference, G-ratio) against established methods.

Main Methods:

  • Developed a Monte Carlo simulation framework based on the G-function.
  • Introduced and evaluated G-area, G-difference, and G-ratio metrics.
  • Compared the novel metrics with the Morisita-Horn Index and likelihood ratio.
  • Validated the framework on external multiplex imaging datasets (colorectal and prostate cancer).

Main Results:

  • The G-function framework effectively detects proximity differences between groups, irrespective of cell abundance.
  • G-area demonstrated the highest accuracy in capturing group-level proximity changes.
  • The framework showed generalizability across different cancer types and imaging platforms.

Conclusions:

  • The proposed G-function framework offers a cell-count-robust approach for spatial analysis in biological tissues.
  • This method enables more reliable detection of microenvironmental changes in disease contexts.
  • The G-area metric is a promising tool for quantitative spatial biology research.