arcos4py 0.3.3

A python package to detect collective spatio-temporal phenomena.

arcos4py

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

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Features

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.

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New in ARCOS with ARCOS.px 🎉

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 plugin plugin

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Notebooks and Reproducible Analysis

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.

Installation

Install from PyPI:

pip install arcos4py

Napari Plugin

Arcos4py is also available as a Napari Plugin arcos-gui. arcos-gui can simplify parameter finding and visualization.

or images directly: arcosPx-napari

Credits

Maciej Dobrzynski created the first version of ARCOS.

This package was created with Cookiecutter and the waynerv/cookiecutter-pypackage project template.