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 refin

SUBMITTER: Williamson DJ 

PROVIDER: S-EPMC7083906 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature