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 language | English (US) |
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Article number | 8070393 |
Pages (from-to) | 622-636 |
Number of pages | 15 |
Journal | IEEE Transactions on Image Processing |
Volume | 27 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2018 |
Externally published | Yes |
Keywords
- Colocalization
- Fluorescence microscopy
- Hypothesis testing
- Nonparametric statistics
- Scan statistics
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design