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The Rényi divergence enables accurate and precise cluster analysis for localization microscopy

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)4102-4111
Number of pages10
JournalBioinformatics (Oxford, England)
Issue number23
Accepted/In press30 May 2018
Published1 Dec 2018

King's Authors


Motivation: Clustering analysis is a key technique for quantitatively characterizing structures in localization microscopy images. To build up accurate information about biological structures, it is critical that the quantification is both accurate (close to the ground truth) and precise (has small scatter and is reproducible). Results: Here, we describe how the Rényi divergence can be used for cluster radius measurements in localization microscopy data. We demonstrate that the Rényi divergence can operate with high levels of background and provides results which are more accurate than Ripley's functions, Voronoi tesselation or DBSCAN. Availability and implementation: The data supporting this research and the software described are accessible at the following site: Correspondence and requests for materials should be addressed to the corresponding author. Supplementary information: Supplementary data are available at Bioinformatics online.

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