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Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image

Suman Majumder1, Brent A Coull2, Jessica L Mark Welch3

  • 1Department of Statistics, University of Missouri, Columbia, Missouri, USA.

Statistics in Medicine
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

We introduce a new statistical model, the multivariate cluster point process (MCPP), to analyze spatial arrangements of multiple object types, like cells. This method accurately quantifies complex clustering patterns, revealing previously unknown biological interactions.

Keywords:
Thomas processimagingmicrobiomeparent‐offspring modelplaquespatial statistics

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Area of Science:

  • Spatial statistics
  • Computational biology
  • Biostatistics

Background:

  • Analyzing spatial relationships of objects, such as cells in biomedical images, is crucial for understanding biological mechanisms.
  • Traditional methods often overlook the central object's role in cluster formation, limiting analysis of complex arrangements.
  • Parent-offspring frameworks offer potential but require adaptation for multi-object systems.

Purpose of the Study:

  • To introduce a novel multivariate cluster point process (MCPP) for quantifying multi-object spatial arrangements.
  • To develop a statistical framework that accounts for multilayered and multivariate clustering, utilizing central 'parent' object locations.
  • To compare the MCPP's performance against existing models using simulated and real-world biological data.

Main Methods:

  • Development of the multivariate cluster point process (MCPP) model, specifying parent and offspring object types.
  • Utilizing the deviance information criterion (DIC) to compare model fits and explore unknown roles of object types.
  • Validation using simulated data to assess accuracy and precision against the Neyman-Scott process model.
  • Application to human dental plaque biofilm image data to quantify known and discover novel spatial relationships.

Main Results:

  • The MCPP accurately identified simulated spatial relationships and provided more precise parameter estimates than the univariate Neyman-Scott process model.
  • In dental plaque data, MCPP quantified clustering of Streptococcus and Porphyromonas around Corynebacterium, and Pasteurellaceae around Streptococcus.
  • The model successfully captured hypothesized structures and suggested a novel clustering relationship between Fusobacterium and Leptotrichia.

Conclusions:

  • The multivariate cluster point process (MCPP) is an effective tool for quantifying complex multi-object spatial arrangements in biological systems.
  • MCPP facilitates the discovery of novel biological interactions by accurately modeling spatial configurations.
  • This approach enhances our understanding of micro-environmental structures and their implications in fields like microbiology and cell biology.