Jacquemet, G.

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, an online platform tailored to simplify the exploration and analysis of tracking data by leveraging the free, cloud-based computational resources of Google Colab. CellTracksColab facilitates the amalgamation and analysis of tracking 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, using Uniform Manifold Approximation and Projection and Hierarchical Density-Based Spatial Clustering of Applications with Noise, enabling researchers to identify distinct behavioral patterns and trends without bias. CellTracksColab is available for the broader scientific community at https://github.com/guijacquemet/CellTracksColab.