Sailem Heba Z, Sero Julia E, Bakal Chris
Dynamical Cell Systems, Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.
Nat Commun. 2015 Jan 8;6:5825. doi: 10.1038/ncomms6825.
Visualization is essential for data interpretation, hypothesis formulation and communication of results. However, there is a paucity of visualization methods for image-derived data sets generated by high-content analysis in which complex cellular phenotypes are described as high-dimensional vectors of features. Here we present a visualization tool, PhenoPlot, which represents quantitative high-content imaging data as easily interpretable glyphs, and we illustrate how PhenoPlot can be used to improve the exploration and interpretation of complex breast cancer cell phenotypes.
可视化对于数据解读、假设形成以及结果交流至关重要。然而，对于通过高内涵分析生成的图像衍生数据集，用于描述复杂细胞表型的可视化方法却很匮乏，其中复杂细胞表型被表示为特征的高维向量。在此，我们展示了一种可视化工具PhenoPlot，它将定量高内涵成像数据表示为易于解读的符号，并且我们说明了如何使用PhenoPlot来改进对复杂乳腺癌细胞表型的探索和解读。
