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Bayesian cluster identification in single-molecule localization microscopy data

Research output: Contribution to journalArticlepeer-review

Patrick Rubin-Delanchy, Garth L. Burn, Juliette Griffié, David J. Williamson, Nicholas A. Heard, Andrew P. Cope, Dylan M. Owen

Original languageEnglish
Pages (from-to)1072-1076
Number of pages5
Issue number11
Early online date5 Oct 2015
Accepted/In press2 Sep 2015
E-pub ahead of print5 Oct 2015
Published1 Nov 2015


King's Authors


Single-molecule localization-based super-resolution microscopy techniques such as photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) produce pointillist data sets of molecular coordinates. Although many algorithms exist for the identification and localization of molecules from raw image data, methods for analyzing the resulting point patterns for properties such as clustering have remained relatively under-studied. Here we present a model-based Bayesian approach to evaluate molecular cluster assignment proposals, generated in this study by analysis based on Ripley's K function. The method takes full account of the individual localization precisions calculated for each emitter. We validate the approach using simulated data, as well as experimental data on the clustering behavior of CD3σ, a subunit of the CD3 T cell receptor complex, in resting and activated primary human T cells.

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