Machine learning for cluster analysis of localization microscopy data

Machine learning for cluster analysis of localization microscopy data

The characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.

Bibliographic Details
Main Authors: David J. Williamson, Garth L. Burn, Sabrina Simoncelli, Juliette Griffié, Ruby Peters, Daniel M. Davis, Dylan M. Owen
Format: Article
Language:English
Published: Nature Publishing Group 2020-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-15293-x
id doaj-5bc574d8c2a043d098ecae44cf7959d5
record_format Article
spelling doaj-5bc574d8c2a043d098ecae44cf7959d52021-05-11T08:36:53ZengNature Publishing GroupNature Communications2041-17232020-03-0111111010.1038/s41467-020-15293-xMachine learning for cluster analysis of localization microscopy dataDavid J. Williamson0Garth L. Burn1Sabrina Simoncelli2Juliette Griffié3Ruby Peters4Daniel M. Davis5Dylan M. Owen6Department of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College LondonDepartment of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College LondonDepartment of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College LondonDepartment of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College LondonDepartment of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College LondonDivision of Infection, Immunity and Respiratory Medicine, University of ManchesterDepartment of Physics and Randall Centre for Cell and Molecular Biophysics, King’s College LondonThe characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.https://doi.org/10.1038/s41467-020-15293-x
collection DOAJ
language English
format Article
sources DOAJ
author David J. Williamson
Garth L. Burn
Sabrina Simoncelli
Juliette Griffié
Ruby Peters
Daniel M. Davis
Dylan M. Owen
spellingShingle David J. Williamson
Garth L. Burn
Sabrina Simoncelli
Juliette Griffié
Ruby Peters
Daniel M. Davis
Dylan M. Owen
Machine learning for cluster analysis of localization microscopy data
Nature Communications
author_facet David J. Williamson
Garth L. Burn
Sabrina Simoncelli
Juliette Griffié
Ruby Peters
Daniel M. Davis
Dylan M. Owen
author_sort David J. Williamson
title Machine learning for cluster analysis of localization microscopy data
title_short Machine learning for cluster analysis of localization microscopy data
title_full Machine learning for cluster analysis of localization microscopy data
title_fullStr Machine learning for cluster analysis of localization microscopy data
title_full_unstemmed Machine learning for cluster analysis of localization microscopy data
title_sort machine learning for cluster analysis of localization microscopy data
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-03-01
description The characterization of clusters in single-molecule microscopy data is vital to reconstruct emerging spatial patterns. Here, the authors present a fast and accurate machine-learning approach to clustering, to address the issues related to the size of the data and to sample heterogeneity.
url https://doi.org/10.1038/s41467-020-15293-x
work_keys_str_mv AT davidjwilliamson machinelearningforclusteranalysisoflocalizationmicroscopydata
AT garthlburn machinelearningforclusteranalysisoflocalizationmicroscopydata
AT sabrinasimoncelli machinelearningforclusteranalysisoflocalizationmicroscopydata
AT juliettegriffie machinelearningforclusteranalysisoflocalizationmicroscopydata
AT rubypeters machinelearningforclusteranalysisoflocalizationmicroscopydata
AT danielmdavis machinelearningforclusteranalysisoflocalizationmicroscopydata
AT dylanmowen machinelearningforclusteranalysisoflocalizationmicroscopydata
_version_ 1721450655403474944

Similar Items