Image co-localization - co-occurrence versus correlation
- PMID: 29439158
- DOI: 10.1242/jcs.211847
Image co-localization - co-occurrence versus correlation
Abstract
Fluorescence image co-localization analysis is widely utilized to suggest biomolecular interaction. However, there exists some confusion as to its correct implementation and interpretation. In reality, co-localization analysis consists of at least two distinct sets of methods, termed co-occurrence and correlation. Each approach has inherent and often contrasting strengths and weaknesses. Yet, neither one can be considered to always be preferable for any given application. Rather, each method is most appropriate for answering different types of biological question. This Review discusses the main factors affecting multicolor image co-occurrence and correlation analysis, while giving insight into the types of biological behavior that are better suited to one approach or the other. Further, the limits of pixel-based co-localization analysis are discussed in the context of increasingly popular super-resolution imaging techniques.
Keywords: Co-localization; Fluorescence microscopy; Image analysis; Manders; Pearson.
© 2018. Published by The Company of Biologists Ltd.
Conflict of interest statement
Competing interestsThe authors declare no competing or financial interests.
Comment in
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Quantifying colocalization: the MOC is a hybrid coefficient - an uninformative mix of co-occurrence and correlation.J Cell Sci. 2019 Jan 9;132(1):jcs222455. doi: 10.1242/jcs.222455. J Cell Sci. 2019. PMID: 30626689 No abstract available.
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The Pearson's correlation coefficient is not a universally superior colocalization metric. Response to 'Quantifying colocalization: the MOC is a hybrid coefficient - an uninformative mix of co-occurrence and correlation'.J Cell Sci. 2019 Jan 9;132(1):jcs227074. doi: 10.1242/jcs.227074. J Cell Sci. 2019. PMID: 30626690 No abstract available.
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