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Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy.

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 (?c). 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.

SUBMITTER: Jiang S

PROVIDER: S-EPMC5479598 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Robust nonparametric quantification of clustering density of molecules in single-molecule localization microscopy.

Jiang Shenghang S Park Seongjin S Challapalli Sai Divya SD Fei Jingyi J Wang Yong Y

PloS one 20170621 6

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) va ...[more]

PMID: 28636661

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