arcos4py is a Python package designed to detect and analyze collective spatiotemporal phenomena in biological imaging data.
- Documentation: https://pertzlab.github.io/arcos4py
- GitHub Repository: https://github.com/pertzlab/arcos4py
- PyPI Package: https://pypi.org/project/arcos4py/
- Free Software License: MIT
Automated Recognition of Collective Signalling for Python (arcos4py) identifies collective spatial events in time-series data or microscopy images. The software tracks waves of protein activity in 2D and 3D cell cultures and follows them over time.
Such collective dynamics have been observed in:
- Epithelial homeostasis (Gagliardi et al., 2020; Takeuchi et al., 2020; Aikin et al., 2020)
- Acinar morphogenesis (Ender et al., 2020)
- Osteoblast regeneration (De Simone et al., 2021)
- Coordination of collective cell migration (Aoki et al., 2017; Hino et al., 2020)
The R package ARCOS (https://github.com/dmattek/ARCOS) provides a similar R implementation. The arcos4py version includes more recent upgrades and added functionality:
- Event tracking directly on image data
- Split/merge detection
- Motion prediction for robust temporal linking
Data format: Long-table format with object coordinates, time, and optionally measurements; or binary image sequences for pixel-level analysis.
Modular API: Use the full ARCOS class or individual tools via arcos.tools. Process binary images directly using track_events_images in arcos4py.tools.
We recently released a major update, ARCOS.px, extending arcos4py to track subcellular dynamic structures like actin waves, podosomes, and focal adhesions directly from binarized time-lapse images.
Publication:
Tracking Coordinated Cellular Dynamics in Time-Lapse Microscopy with ARCOS.px. bioRxiv
What’s new:
- Pixel-based tracking of discontinuous, irregular structures
- Lineage tracking across merges and splits
- Optional Motion prediction and frame-to-frame linking with optimal transport
- Support for DBSCAN and HDBSCAN clustering and custom clustering methods
- Improved memory usage and lazy evaluation for long time series
- Integrated into Napari via the
arcosPx-napari pluginplugin
To facilitate reproducibility and provide practical examples, we have made available a collection of Jupyter notebooks that demonstrate the use of ARCOS.px in various scenarios. These notebooks cover:
Wave Simulation: Scripts to simulate circular & directional waves, and target & chaotic patterns using cellular automaton.
Synthetic RhoA Activity Wave: Analysis of optogenetically induced synthetic RhoA activity waves.
Podosome Dynamics: Tracking and analysis of podosome-like structures under different conditions.
Actin Wave Tracking: Tracking and analysis of actin waves in 2D and extractin temporal order.
You can access these notebooks in the ARCOSpx-publication repository under the scripts directory.
Install from PyPI:
pip install arcos4pyArcos4py is also available as a Napari Plugin arcos-gui. arcos-gui can simplify parameter finding and visualization.
or images directly: arcosPx-napari
Maciej Dobrzynski created the first version of ARCOS.
This package was created with Cookiecutter and the waynerv/cookiecutter-pypackage project template.