Bayesian cluster identification in single-molecule localization microscopy data.
File(s)
Accepted version
Author(s)
Type
Journal Article
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.
Date Issued
2015-10-05
Date Acceptance
2015-09-02
Citation
Nature Methods, 2015, 12, pp.1072-1076
ISSN
1548-7105
Publisher
Nature Publishing Group
Start Page
1072
End Page
1076
Journal / Book Title
Nature Methods
Volume
12
Copyright Statement
© 2015, Rights Managed by Nature Publishing Group
Identifier
PII: nmeth.3612
Publication Status
Published