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

Adela D. Staszowska, Patrick Fox-Roberts, Liisa M. Hirvonen, Christopher J. Peddie, Lucy M. Collinson, Gareth E. Jones, Susan Cox

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4102-4111
Number of pages10
JournalBioinformatics
Volume34
Issue number23
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

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