The Rényi divergence enables accurate and precise cluster analysis for localization microscopy.

The Rényi divergence enables accurate and precise cluster analysis for localization microscopy.
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DOI:
10.1093/bioinformatics/bty403
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发表时间:
2018-12-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
通讯作者:
Cox S
Cox S
中科院分区:
其他
文献类型:
--
作者:
Staszowska AD;Fox-Roberts P;Hirvonen LM;Peddie CJ;Collinson LM;Jones GE;Cox S

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聚类分析是定量描述定位显微图像中结构的关键技术。为了建立关于生物结构的准确信息,至关重要的是量化既要准确(接近基本事实),又要精确(分散性小,可重复性)。在这里,我们描述了如何在局域显微镜数据中使用Rényi发散来测量团簇半径。我们证明了Rényi散度可以在高水平的背景下运行,并且提供了比Ripley函数、Voronoi细分或DBSCAN更精确的结果。支持这项研究的数据和所描述的软件可从以下网站获得:https://dx.doi.org/10.18742/RDM01-316.函件和索取材料的要求应寄给相应的作者。补充数据可在生物信息学在线上获得。
Clustering analysis is a key technique for quantitatively characterizing structures in localization microscopy images. To build up accurate information about biological structures, it is critical that the quantification is both accurate (close to the ground truth) and precise (has small scatter and is reproducible). Here, we describe how the Rényi divergence can be used for cluster radius measurements in localization microscopy data. We demonstrate that the Rényi divergence can operate with high levels of background and provides results which are more accurate than Ripley’s functions, Voronoi tesselation or DBSCAN. The data supporting this research and the software described are accessible at the following site: https://dx.doi.org/10.18742/RDM01-316. Correspondence and requests for materials should be addressed to the corresponding author. Supplementary data are available at Bioinformatics online.
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影响因子: 16.6
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