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Nat Methods. 2015 Nov;12(11):1072-6. doi: 10.1038/nmeth.3612. Epub 2015 Oct 5.

Bayesian cluster identification in single-molecule localization microscopy data.

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School of Mathematics, Heilbronn Institute for Mathematical Research, University of Bristol, Bristol, UK.
Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College London, London, UK.
Manchester Collaborative Centre for Inflammation Research, University of Manchester, Manchester, UK.
Department of Mathematics, Imperial College London, London, UK.
Division of Immunology, Infection and Inflammatory Disease, Academic Department of Rheumatology, King's College London, London, UK.


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 CD3ζ, a subunit of the CD3 T cell receptor complex, in resting and activated primary human T cells.

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