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Cluster analysis of SMLM data and analysis of the distances between nearest neighbouring clusters

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 20 Jun 2023Publisher:Zenodo
Authors: Gidden, Zoe;

doi: 10.5281/zenodo.8359460 , 10.5281/zenodo.8060555 , 10.5281/zenodo.8060554

Cluster analysis of SMLM data and analysis of the distances between nearest neighbouring clusters

Abstract

Python code that reads GDSCSMLM FitResults csv files and runs DBSCAN cluster analysis on the data. A nearest neighbour algorithm is then applied to identify and calculate the distance to the nearest neighbouring cluster to each cluster. The distances between clusters identified for each file and for all the files in a folder are displayed in histograms. In addition, a confidence ellipse is fitted to each cluster to enable the calculation of the eccentricity and the maximum length of the cluster. This code was written as part of ongoing research of the LIVE-PAINT super-resolution imaging technique in the Regan and Horrocks Labs at the University of Edinburgh.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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