Imperial College London

Professor Nick Heard

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics



+44 (0)20 7594 1490n.heard Website




543Huxley BuildingSouth Kensington Campus






BibTex format

author = {Rubin-Delanchy, P and Burn, GL and Griffié, J and Williamson, DJ and Heard, NA and Cope, AP and Owen, DM},
doi = {10.1038/nmeth.3612},
journal = {Nature Methods},
pages = {1072--1076},
title = {Bayesian cluster identification in single-molecule localization microscopy data.},
url = {},
volume = {12},
year = {2015}

RIS format (EndNote, RefMan)

AB - 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.
AU - Rubin-Delanchy,P
AU - Burn,GL
AU - Griffié,J
AU - Williamson,DJ
AU - Heard,NA
AU - Cope,AP
AU - Owen,DM
DO - 10.1038/nmeth.3612
EP - 1076
PY - 2015///
SN - 1548-7105
SP - 1072
TI - Bayesian cluster identification in single-molecule localization microscopy data.
T2 - Nature Methods
UR -
UR -
VL - 12
ER -