ArticleBioinformatics (Oxford, England)2018
The Rényi divergence enables accurate and precise cluster analysis for localization microscopy.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The phosphoproteomic landscape of the neurological manifestations in tuberous sclerosis complex.Acta neuropathologica · 2026Article
- Development of a green reversibly photoswitchable variant of Eos fluorescent protein with fixation resistance.Molecular biology of the cell · 2021Article
- Verifying molecular clusters by 2-color localization microscopy and significance testing.Scientific reports · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
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: https://dx.doi.org/10.18742/RDM01-316. 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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