Machine learning for cluster analysis of localization microscopy data

Autor: David J. Williamson, Garth L. Burn, Sabrina Simoncelli, Juliette Griffié, Ruby Peters, Daniel M. Davis, Dylan M. Owen

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-10 (2020)

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

Externí odkaz: https://doaj.org/article/5bc574d8c2a043d098ecae44cf7959d5

Machine learning for cluster analysis of localization microscopy data

Autor: Daniel M. Davis, Dylan M. Owen, Sabrina Simoncelli, Ruby Peters, David Williamson, Juliette Griffié, Garth L. Burn

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
Nature Communications

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fe5c02ca0062d972cfa2254d2d19ac9
http://link.springer.com/article/10.1038/s41467-020-15293-x

Machine-learning for cluster analysis of localization microscopy data

Autor: Daniel M. Davis, Dylan M. Owen, David Williamson, Garth L. Burn, Juliette Griffié

Quantifying the clustering of points within single-molecule localization microscopy data is useful to understanding the spatial relationships of the molecules in the underlying sample. The conversion of point pattern data into a meaningful descriptio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a8216e03672262a8afb42680ab96e91

A switch-variant model integrates the functions of an autoimmune variant of the phosphatase PTPN22

Autor: Garth L. Burn, Torkel Vang, Jimmi Nielsen

Publikováno v: Science signaling. 11(526)

The R620W polymorphism in protein tyrosine phosphatase nonreceptor type 22 (PTPN22) predisposes carriers to several autoimmune diseases. Two papers in Science Immunology and Science Signaling on this human disease–associated variant lead us to prop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6edfef358be05b89e9a6756eb3750abe
https://pubmed.ncbi.nlm.nih.gov/29666305

3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse

Autor: Michael Shannon, Dylan M. Owen, Ruby Peters, Lies Boelen, Juliette Griffié, Garth L. Burn, Andrew P. Cope, Leigh Shlomovich, David Williamson, Satya Khuon, Patrick Rubin-Delanchy, Jesse Aaron, Edward A. K. Cohen

Publikováno v: Griffié, J, Shlomovich, L, Williamson, D J, Shannon, M, Aaron, J, Khuon, S, Burn, G L, Boelen, L, Peters, R, Cope, A P, Cohen, E A K, Rubin-Delanchy, P & Owen, D M 2017, ' 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse ', Scientific Reports, vol. 7, no. 1, 4077 . https://doi.org/10.1038/s41598-017-04450-w
Scientific Reports
Griffié, J, Shlomovich, L, Williamson, D, Shannon, M, Aaron, J, Khuon, S, Burn, G, Boelen, L, Peters, R, Cope, A, Cohen, E, Owen, D & Rubin-Delanchy, P 2017, ' 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse ', Scientific Reports, vol. 7, 4077 . https://doi.org/10.1038/s41598-017-04450-w
Scientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)

Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10–30 nm, revealing the cell’s nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c15fbc3b3d18e3713a8157eb404c6fd5
https://kclpure.kcl.ac.uk/ws/files/73346811/3D_Bayesian_cluster_analysis_GRIFFIE_Publishedonline22June2017_GOLD_VoR_CC_BY_.pdf