Automated and Robust Quantification of Colocalization in Dual-Color Fluorescence Microscopy: A Nonparametric Statistical Approach

TL;DR

This paper introduces a new nonparametric statistical method for more robust and reliable quantification of colocalization in dual-color fluorescence microscopy, improving upon existing techniques.

Contribution

It proposes a novel colocalization metric based on nonparametric and scan statistics, with demonstrated effectiveness on benchmark and biological datasets.

Findings

The new metric is robust and statistically rigorous.

It outperforms existing methods in benchmark tests.

Application to biological data confirms practical usefulness.

Abstract

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.