Sailem Heba Z, Cooper Sam, Bakal Chris
a Department of Engineering Science , University of Oxford , Oxford , UK .
b Department of Computational Systems Medicine , Imperial College, South Kensington Campus , London , UK , and.
Crit Rev Biochem Mol Biol. 2016;51(2):96-101. doi: 10.3109/10409238.2016.1146222. Epub 2016 Feb 24.
Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.
数据可视化是科学的一个基本方面。在基于显微镜的研究背景下，可视化通常涉及图像本身的呈现。然而，当显微镜实验需要对数百万个细胞进行成像，并且以高内涵方式对复杂的细胞表型进行量化时，数据可视化就具有挑战性。大多数成熟的可视化工具都不适用于显示高内涵数据，这推动了新的可视化方法的发展。在这篇综述中，我们讨论了在经典显微镜研究和高内涵显微镜研究中数据是如何被可视化的；以及不同可视化方法的优缺点。
