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

Shulei Wang, Ellen T. Arena, Kevin W. Eliceiri, Ming Yuan

Research output: Contribution to journalArticlepeer-review

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 data sets, as well as biological examples, further demonstrates the usefulness of the proposed technique.

Original languageEnglish (US)
Article number8070393
Pages (from-to)622-636
Number of pages15
JournalIEEE Transactions on Image Processing
Volume27
Issue number2
DOIs
StatePublished - Feb 2018
Externally publishedYes

Keywords

  • Colocalization
  • Fluorescence microscopy
  • Hypothesis testing
  • Nonparametric statistics
  • Scan statistics

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Fingerprint

Dive into the research topics of 'Automated and Robust Quantification of Colocalization in Dual-Color Fluorescence Microscopy: A Nonparametric Statistical Approach'. Together they form a unique fingerprint.

Cite this