ARCOS
ARCOS - Automated Recognition of Correlated Structures
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ARCOS - Automated Recognition of Correlated Structures
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ARCOS is a computational method to detect and quantify collective, spatio-temporally correlated phenomena. The algorithm identifies and tracks spatial clusters in time-lapse images. Although designed to analyze signalling phenomena in biological cells or cell collectives, it is applicable to other systems even outside of the realm of cell biology.
We provide open-source implementations of ARCOS for:
Both code bases can handle segmented data and raster images, although the Python version contains additional optimizations and features to handle large datasets.
The napari plugins enables anyone without extensive programming knowledge to explore parameters through an intuitive GUI on a platform that emerges as a de-facto standard for viewing multidimensional images.
If you use this method in your research, please cite our papers:
and if you used the tutorial please also cite:
Learn the fundamentals of ARCOS to get a deeper understanding of our main features:
Additionally, two dedicated interactive plugins for are available:
− to handle data from image segmentation
− to handle raster images
Demo of the arcos-gui napari plugin ()
Demo of the entire workflow from raw images to detection of collective events ()
The original ARCOS algorithm is now published in the Journal of Cell Biology (JCB):
For a complete tutorial on how to use ARCOS from raw images to full analysis refer to our publication in Methods in Microscopy (MiM):
and the corresponding containing a detailed notebook and installation instructions.
