Jiang Shenghang, Park Seongjin, Challapalli Sai Divya, Fei Jingyi, Wang Yong
Department of Physics, University of Arkansas, Fayetteville, Arkansas, 72701, United States of America.
Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois, 60637, United States of America.
PLoS One. 2017 Jun 21;12(6):e0179975. doi: 10.1371/journal.pone.0179975. eCollection 2017.
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.
我们报告了一种强大的非参数描述符J'(r)，用于量化单分子定位显微镜中聚类分子的密度。基于最近邻分布函数的J'(r)在分析点模式时不需要任何参数作为输入。我们表明，在分子簇存在的情况下，J'(r)呈现出谷形，并且该谷的特征可靠地反映了数据中的聚类特征。最重要的是，J'(r)谷的位置（[公式：见正文]）仅取决于聚类分子的密度(ρc)。因此，它是直接估计单分子定位显微镜中分子聚类密度的理想选择。例如，该描述符被用于估计大肠杆菌中ptsG mRNA的聚类密度。
