CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures

Bioinformatics
Bruce A CorlissShayn M Peirce

Abstract

Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure. We present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test ...Continue Reading

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Citations

Apr 24, 2020·Diabetes·Bruce A CorlissShayn M Peirce

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Methods Mentioned

BETA
flow
fluorescence microscopy

Software Mentioned

MATLAB
CDNF
CIRCOAST
CIRCOAST GUI

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