In life sciences, tracking objects from movies is pivotal for quantifying behaviors of particles, organelles, bacteria, cells, and whole animals. CellTracksColab bridges the gap between tracking and insightful analysis. CellTracksColab simplifies the journey from data compilation to analysis. Built on the Google Colaboratory framework, it provides a cloud-based solution accessible with just a web browser and a Google account.

• 📘 Holistic View: Comprehensive analysis across fields of view, biological repeats, and conditions.
• 🖥️ User-Centric: Intuitive GUI designed for all users.
• 🔍 Visualization: Track visualization and filtering.
• 📊 Analysis: Deep-dive into track metrics and statistics.
• 🧪 Reliability: Check experimental variability using hierarchical clustering.
• 🔧 Advanced Tools: Harness the power of UMAP, t-SNE and HDBSCAN.
• 💼 Flexibility: Tailor and adapt to your needs.

To begin your analysis journey, click the "Open In Colab" button below, corresponding to your data type. For a seamless experience, right-click the button and select "Open in a new tab."

1. For TrackMate Data:

   • Delve into your TrackMate data with our specialized notebook.
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2. Analysis for Other Data Types:

   • Explore our general-purpose notebook for analyzing diverse tracking datasets.
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Maximize your analysis with our supplementary tools and modules.

• CellTracksColab - Viewer:
  • Ideal for loading datasets in CellTracksColab format or sharing data with colleagues.
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• CellTracksColab - TrackMate - Plate:
  • This notebook can handle TrackMate data structured in a plate format, such as file names commonly produced by incubator microscopes like Incucytes.
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• CellTracksColab - Track Clustering Analysis:

  • Dive deeper into your dataset with our track clustering analysis module.
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• CellTracksColab - Distance to ROI:

  • This notebook is specifically designed to analyze movement tracks in relation to designated Regions of Interest (ROIs). Its primary aim is to compute and analyze the distances between moving objects (tracks) and ROIs, which may also be dynamic.
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• More to come soon

CellTracksColab is inspired by several key projects in cell tracking and analysis. We acknowledge the influential contributions of Traject3d, CellPhe, CelltrackR, the MotilityLab website, and Cellplato on Zenodo. The innovative use of UMAP and HDBSCAN for analyzing tracking data, as featured in CellTracksColab, was first introduced in CellPlato.

• Test Dataset: Start exploring with our test dataset.
• Data Structure: Organize with our two-tiered folder hierarchy. Details here.
• Data Requirements: Note that CellTracksColab does not yet support track merging or splitting.

Dive deeper. Visit our comprehensive wiki.

Figure 1: Compile your data	Figure 2: Visualise your tracks
Figure 3: Compute additional metrics	Figure 4: Plot track parameters
Figure 5: Compute Similarity Metrics between Field of Views and between Conditions and Repeats	Figure 6: Perform UMAP
Figure 7: Identify clusters using HDBSCAN	Figure 8: Understand your clusters using a heatmap

Created by Guillaume Jacquemet.

We welcome your insights and improvements. Raise an issue here.

Licensed under the MIT License. Details here.

If you use CellTracksColab in your research, please cite the following paper:

Guillaume Jacquemet. (2023). CellTracksColab—A platform for compiling, analyzing, and exploring tracking data. bioRxiv. https://doi.org/10.1101/2023.10.20.563252

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