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

Colleges, School and Institutes

External organisations

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


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.


Original languageEnglish
Pages (from-to)1072-1076
Number of pages5
JournalNature Methods
Early online date1 Nov 2015
Publication statusE-pub ahead of print - 1 Nov 2015