Machine learning for cluster analysis of localization microscopy data

David J. Williamson, Garth L. Burn, Sabrina Simoncelli, Juliette Griffié, Ruby Peters, Daniel M. Davis and Dylan M. Owen ()
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David J. Williamson: Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London
Garth L. Burn: Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London
Sabrina Simoncelli: Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London
Juliette Griffié: Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London
Ruby Peters: Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London
Daniel M. Davis: University of Manchester
Dylan M. Owen: Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-15293-x

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