在生命科学中,跟踪电影中的物体使研究人员能够量化单个粒子的行为,细胞器,细菌,细胞,甚至整个动物。虽然许多工具现在允许从视频自动跟踪,编译仍然存在重大挑战,分析,并探索由这些方法生成的大型数据集。这里,我们介绍CellTracksColab,为简化细胞跟踪数据的探索和分析而量身定制的平台。CellTracksColab有助于跨多个视野对结果进行编译和分析,条件,并重复,确保完整的数据集概览。CellTracksColab还利用高维数据缩减和聚类的力量,使研究人员能够毫无偏见地识别不同的行为模式和趋势。最后,CellTracksColab还包括支持空间分析的专门分析模块(聚类,接近特定的感兴趣区域)。我们用3个用例演示了CellTracksColab功能,包括T细胞和癌细胞迁移,以及丝状体动力学。CellTracksColab可用于更广泛的科学界,网址为https://github.com/CellMigrationLab/CellTracksColab。
In life sciences, tracking objects from movies enables researchers to quantify the behavior of single particles, organelles, bacteria, cells, and even whole animals. While numerous tools now allow automated tracking from video, a significant challenge persists in compiling, analyzing, and exploring the large datasets generated by these approaches. Here, we introduce CellTracksColab, a platform tailored to simplify the exploration and analysis of cell tracking data. CellTracksColab facilitates the compiling and analysis of results across multiple fields of view, conditions, and repeats, ensuring a holistic dataset overview. CellTracksColab also harnesses the power of high-dimensional data reduction and clustering, enabling researchers to identify distinct behavioral patterns and trends without bias. Finally, CellTracksColab also includes specialized analysis modules enabling spatial analyses (clustering, proximity to specific regions of interest). We demonstrate CellTracksColab capabilities with 3 use cases, including T cells and cancer cell migration, as well as filopodia dynamics. CellTracksColab is available for the broader scientific community at https://github.com/CellMigrationLab/CellTracksColab.