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Machine learning for cluster analysis of localization microscopy data
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DOI：10.1038/s41467-020-15293-x.png)
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摘要
En 中文
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. 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.
Keyword:
T-CELL-ACTIVATION
GROUND-STATE-DEPLETION
SUPERRESOLUTION MICROSCOPY
KINASE CSK
PAG
PROTEIN
PALM
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期刊
IF:
15.7
论文数:
9.4W
被引数:
91.2W
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引用论文
Bayesian cluster identification in single-molecule Localization microscopy data.png)
NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏A Bayesian cluster analysis method for single-molecule localization microscopy dataNATURE PROTOCOLS
NATURE PROTOCOLSIF16
PREAI
分享收藏The Csk-Associated Adaptor PAG Inhibits Effector T Cell Activation in Cooperation with Phosphatase PTPN22 and Dok AdaptorsCsk相关衔接子PAG与磷酸酶PTPN22和Dok衔接子协同抑制效应T细胞活化CELL REPORTSOAAI
CELL REPORTSIF6.9
OAAI分享收藏Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)通过随机光学重建显微镜 (STORM) 进行亚衍射极限成像NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏Pre-existing clusters of the adaptor Lat do not participate in early T cell signaling eventsNATURE IMMUNOLOGY
NATURE IMMUNOLOGYIF27.6
PREAI
分享收藏Ultra-high resolution imaging by fluorescence photoactivation localization microscopy荧光光活化定位显微镜的超高分辨率成像BIOPHYSICAL JOURNALOAAI
BIOPHYSICAL JOURNALIF3.1
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