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- Title
Bayesian cluster identification in single-molecule localization microscopy data.
- Authors
Rubin-Delanchy, Patrick; Burn, Garth L; Griffié, Juliette; Williamson, David J; Heard, Nicholas A; Cope, Andrew P; Owen, Dylan M
- Abstract
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.
- Subjects
SINGLE molecules; BAYESIAN analysis; HIGH resolution imaging; T cells; MICROSCOPY
- Publication
Nature Methods, 2015, Vol 12, Issue 11, p1072
- ISSN
1548-7091
- Publication type
Article
- DOI
10.1038/nmeth.3612