  Colocalization is a powerful tool to study the interactions between
fluorescently labeled molecules in biological fluorescence microscopy. However,
existing techniques for colocalization analysis have not undergone continued
development especially in regards to robust statistical support. In this paper,
we examine two of the most popular quantification techniques for colocalization
and argue that they could be improved upon using ideas from nonparametric
statistics and scan statistics. In particular, we propose a new colocalization
metric that is robust, easily implementable, and optimal in a rigorous
statistical testing framework. Application to several benchmark datasets, as
well as biological examples, further demonstrates the usefulness of the
proposed technique.
