Spatially Adaptive Colocalization Analysis in Dual-Color Fluorescence Microscopy

Shulei Wang, Ellen T. Arena, Jordan T. Becker, William M. Bement, Nathan M. Sherer, Kevin W. Eliceiri, Ming Yuan

Research output: Contribution to journalArticlepeer-review

Abstract

Colocalization analysis aims to study complex spatial associations between bio-molecules via optical imaging techniques. However, existing colocalization analysis workflows only assess an average degree of colocalization within a certain region of interest and ignore the unique and valuable spatial information offered by microscopy. In the current work, we introduce a new framework for colocalization analysis that allows us to quantify colocalization levels at each individual location and automatically identify pixels or regions where colocalization occurs. The framework, referred to as spatially adaptive colocalization analysis (SACA), integrates a pixel-wise local kernel model for colocalization quantification and a multi-scale adaptive propagation-separation strategy for utilizing spatial information to detect colocalization in a spatially adaptive fashion. Applications to simulated and real biological datasets demonstrate the practical merits of SACA in what we hope to be an easily applicable and robust colocalization analysis method. In addition, the theoretical properties of SACA are investigated to provide rigorous statistical justification.

Original languageEnglish (US)
Article number8681436
Pages (from-to)4471-4485
Number of pages15
JournalIEEE Transactions on Image Processing
Volume28
Issue number9
DOIs
StatePublished - Sep 2019
Externally publishedYes

Keywords

  • Colocalization
  • fluorescence microscopy
  • hypothesis testing
  • kernel method
  • multiple testing
  • nonparametric statistics

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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