The following study was conducted by Scientists from Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College London, London, UK; London Centre for Nanotechnology and Department of Chemistry, University College London, London, UK; Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK; Institute of Immunology and Immunotherapy, Department of Mathematics and Centre for Membrane Proteins and Receptors, University of Birmingham, Birmingham, UK. Study is published in Nature Communications as detailed below.
Nature Communications; Volume 11, Article Number: 1493; (2020)
Machine Learning for Cluster Analysis of Localization Microscopy Data
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.
Source:
Nature Communications
URL: https://www.nature.com/articles/s41467-020-15293-x
Citation:
Williamson, D. J., G. L. Burn, et al. (2020). “Machine learning for cluster analysis of localization microscopy data.” Nature Communications 11(1): 1493.
