Williamson, DJ;
Burn, GL;
Simoncelli, S;
Griffié, J;
Peters, R;
Davis, DM;
Owen, DM;
(2020)
Machine learning for cluster analysis of localization microscopy data.
Nature Communications
, 11
, Article 1493. 10.1038/s41467-020-15293-x.
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Abstract
Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationships between molecules in the underlying sample. Many existing computational approaches are limited in their ability to process large-scale data sets, to deal effectively with sample heterogeneity, or require subjective user-defined analysis parameters. Here, we develop a supervised machine-learning approach to cluster analysis which is fast and accurate. Trained on a variety of simulated clustered data, the neural network can classify millions of points from a typical single-molecule localization microscopy data set, with the potential to include additional classifiers to describe different subtypes of clusters. The output can be further refined for the measurement of cluster area, shape, and point-density. We demonstrate this approach on simulated data and experimental data of the kinase Csk and the adaptor PAG in primary human T cell immunological synapses.
Type: | Article |
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Title: | Machine learning for cluster analysis of localization microscopy data |
Location: | England |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1038/s41467-020-15293-x |
Publisher version: | https://doi.org/10.1038/s41467-020-15293-x |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Fluorescence imaging, Software |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > London Centre for Nanotechnology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10094099 |
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