ArticlePloS one2017
Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- K-Neighbourhood Analysis: A Method for Understanding SMLM Images as Compositions of Local Neighbourhoods.Frontiers in bioinformatics · 2021Article
- SMoLR: visualization and analysis of single-molecule localization microscopy data in R.BMC bioinformatics · 2019Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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Abstract
We report a robust nonparametric descriptor, J'(r), for quantifying the density of clustering molecules in single-molecule localization microscopy. J'(r), based on nearest neighbor distribution functions, does not require any parameter as an input for analyzing point patterns. We show that J'(r) displays a valley shape in the presence of clusters of molecules, and the characteristics of the valley reliably report the clustering features in the data. Most importantly, the position of the J'(r) valley ([Formula: see text]) depends exclusively on the density of clustering molecules (ρc). Therefore, it is ideal for direct estimation of the clustering density of molecules in single-molecule localization microscopy. As an example, this descriptor was applied to estimate the clustering density of ptsG mRNA in E. coli bacteria.
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