CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures
Author(s) -
Bruce A. Corliss,
H. Clifton Ray,
James T. Patrie,
Jennifer Mansour,
Sam Kesting,
Janice H Park,
Gustavo K. Rohde,
Paul Yates,
Kevin A. Janes,
Shayn M. Peirce
Publication year - 2018
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bty638
Subject(s) - colocalization , biology , computer science , microbiology and biotechnology , computational biology , physics , biological system
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.
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