International Research Training Group 'Computational Methods for the Analysis of the Diversity and Dynamics of Genomes', Faculty of Technology, Bielefeld University, Bielefeld, Germany.
Biodata Mining Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
Bioinformatics. 2019 May 15;35(10):1802-1804. doi: 10.1093/bioinformatics/bty889.
Live cell imaging plays a pivotal role in understanding cell growth. Yet, there is a lack of visualization alternatives for quick qualitative characterization of colonies.
SeeVis is a Python workflow for automated and qualitative visualization of time-lapse microscopy data. It automatically pre-processes the movie frames, finds particles, traces their trajectories and visualizes them in a space-time cube offering three different color mappings to highlight different features. It supports the user in developing a mental model for the data. SeeVis completes these steps in 1.15 s/frame and creates a visualization with a selected color mapping.
https://github.com/ghattab/seevis/.
Supplementary data are available at Bioinformatics online.
活细胞成像在理解细胞生长中起着关键作用。然而，对于快速定性描述菌落，还缺乏可视化的替代方法。
SeeVis 是一个用于自动和定性可视化延时显微镜数据的 Python 工作流程。它可以自动预处理电影帧，找到粒子，追踪它们的轨迹，并在时空立方体中可视化，提供三种不同的颜色映射来突出不同的特征。它支持用户为数据开发心理模型。SeeVis 可以在 1.15 秒/帧的时间内完成这些步骤，并创建一个具有选定颜色映射的可视化效果。
https://github.com/ghattab/seevis/。
补充数据可在“Bioinformatics”在线获取。
