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

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...

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Bibliographic Details
Main Authors: Shenghang Jiang, Seongjin Park, Sai Divya Challapalli, Jingyi Fei, Yong Wang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5479598?pdf=render
Description
Summary: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.
ISSN:1932-6203