Multivariate cluster point process to quantify and explore multi-entity configurations: Application to biofilm image data
arxiv(2022)
摘要
Clusters of similar or dissimilar objects are encountered in many fields.
Frequently used approaches treat the central object of each cluster as latent.
Yet, often objects of one or more types cluster around objects of another type.
Such arrangements are common in biomedical images of cells, in which nearby
cell types likely interact. Quantifying spatial relationships may elucidate
biological mechanisms. Parent-offspring statistical frameworks can be usefully
applied even when central objects (parents) differ from peripheral ones
(offspring). We propose the novel multivariate cluster point process (MCPP) to
quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used
approaches, the MCPP exploits locations of the central parent object in
clusters. It accounts for possibly multilayered, multivariate clustering. The
model formulation requires specification of which object types function as
cluster centers and which reside peripherally. If such information is unknown,
the relative roles of object types may be explored by comparing fit of
different models via the deviance information criterion (DIC). In simulated
data, we compared DIC of a series of models; the MCPP correctly identified
simulated relationships. It also produced more accurate and precise parameter
estimates than the classical univariate Neyman-Scott process model. We also
used the MCPP to quantify proposed configurations and explore new ones in human
dental plaque biofilm image data. MCPP models quantified simultaneous
clustering of Streptococcus and Porphyromonas around Corynebacterium and of
Pasteurellaceae around Streptococcus and successfully captured hypothesized
structures for all taxa. Further exploration suggested the presence of
clustering between Fusobacterium and Leptotrichia, a previously unreported
relationship.
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