A napari plugin to detect and visualize collective signaling events

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    A napari plugin to detect and visualize collective signaling events

    Automated Recognition of Collective Signalling (ARCOS) is an algorithm to identify collective spatial events in time series data, that was written by Maciej Dobrzynski ( It is available as an R (ARCOS) and python (arcos4py) package. ARCOS can identify and visualize collective protein activation in 2- and 3D cell cultures over time.

    This plugin integrates ARCOS into napari. Users can import tracked time-series data in CSV format. The plugin provides GUI elements to process this data with ARCOS. Layers containing the detected collective events are subsequently added to the viewer.

    Following analysis, the user can export the output as a CSV file with the detected collective events or as a sequence of images to generate a movie.


    You can install arcos-gui via pip:

    pip install arcos-gui

    System Requirements

    Since version "0.0.2" this plugin is python native with the arcos4py package available.

    Demo Video



    Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.


    Distributed under the terms of the BSD-3 license, "arcos-gui" is free and open-source software


    If you encounter any problems, please file an issue along with a detailed description.

    This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.


    • 0.0.5

    Release date:

    • 24 June 2022

    First released:

    • 24 February 2022


    • BSD-3-Clause

    Supported data:

    • Information not submitted

    Plugin type:

    • Information not submitted

    GitHub activity:

    • Stars: 1
    • Forks: 1
    • Issues + PRs: 0

    Python versions supported:

    Operating system:


    • arcos4py (>=0.1.2)
    • magicgui (>=0.3.0)
    • matplotlib (>=3.3.4)
    • napari (>=0.4.14)
    • numpy (>=1.21.5)
    • pandas (>=1.3.5)
    • scikit-image (>=0.18.1)
    • scipy (>=1.7.3)

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