Research & Publications

Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.

Research areas

Research areas

Systems Biology

Statistical genomics and Epidemiology

Medical Imaging

Precision and Stratified Medicine

Analysis of clinical trials, observational and longitudinal studies

Infectious Disease Epidemiology

Publications

Citation

BibTex format

@article{Griffie:2017:10.1038/s41598-017-04450-w,
author = {Griffie, J and Shlomovich, L and Williamson, D and Shannon, M and Aarons, J and Khuon, S and Burn, G and Boelen, L and Peters, R and Cope, A and Cohen, E and Rubin-Delanchy, P and Owen, D},
doi = {10.1038/s41598-017-04450-w},
journal = {Scientific Reports},
title = {3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse},
url = {http://dx.doi.org/10.1038/s41598-017-04450-w},
volume = {7},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10–30 nm, revealing the cell’s nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique insight into cellular machinery. Although cluster analysis techniques have been developed for 2D SMLM data sets, few have been applied to 3D. This lack of quantification tools can be explained by the relative novelty of imaging techniques such as interferometric photo-activated localisation microscopy (iPALM). Also, existing methods that could be extended to 3D SMLM are usually subject to user defined analysis parameters, which remains a major drawback. Here, we present a new open source cluster analysis method for 3D SMLM data, free of user definable parameters, relying on a model-based Bayesian approach which takes full account of the individual localisation precisions in all three dimensions. The accuracy and reliability of the method is validated using simulated data sets. This tool is then deployed on novel experimental data as a proof of concept, illustrating the recruitment of LAT to the T-cell immunological synapse in data acquired by iPALM providing ~10 nm isotropic resolution.Introduction.
AU  - Griffie,J
AU  - Shlomovich,L
AU  - Williamson,D
AU  - Shannon,M
AU  - Aarons,J
AU  - Khuon,S
AU  - Burn,G
AU  - Boelen,L
AU  - Peters,R
AU  - Cope,A
AU  - Cohen,E
AU  - Rubin-Delanchy,P
AU  - Owen,D
DO  - 10.1038/s41598-017-04450-w
PY  - 2017///
SN  - 2045-2322
TI  - 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-017-04450-w
UR  - http://hdl.handle.net/10044/1/49681
VL  - 7
ER  -
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