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.
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2020-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-15293-x |
| Summary: | 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. |
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| ISSN: | 2041-1723 |