arcos4py 0.1.0

A python package to detect collective spatio-temporal phenomena.

arcos4py

A python package to detect collective Spatio-temporal phenomena Package is currently in testing phase, i.e. additional features will be added such as additional plotting functionallity. This also means that functionallity might change in the feature.

• Documentation: https://bgraedel.github.io/arcos4py
• GitHub: https://github.com/bgraedel/arcos4py
• PyPI: https://pypi.org/project/arcos4py/
• Free software: MIT

Features

Automated Recognition of Collective Signalling (arcos4py) is a python port of the R package ARCOS (https://github.com/dmattek/ARCOS ) to identify collective spatial events in time series data. The software identifies collective protein activation in 2- and 3D cell cultures over time. Such collective waves have been recently identified in various biological systems. They have been demonstrated to play an important role in the maintenance of epithelial homeostasis (Gagliardi et al., 2020, Takeuchi et al., 2020, Aikin et al., 2020), in the acinar morphogenesis (Ender et al., 2020), osteoblast regeneration (De Simone et al., 2021), and in the coordination of collective cell migration (Aoki et al., 2017, Hino et al., 2020).

Despite its focus on cell signaling, the framework can also be applied to other spatially correlated phenomena that occur over time.

Todo's

• Add additionall plotting functions such as collective event duration, noodle plots for collective id tracks, measurment histogram etc.
• Add additionall tests for binarization and de-biasing modules.
• Add example processing to documentation with images of collective events.

Data Format

Time series should be arranged in "long format" where each row defines the object's location, time, and optionally the measurement value.

ARCOS defines an ARCOS object on which several class methods can be used to prepare the data and calculate collective events. Optionally the objects used in the ARCOS class can be used individually by importing them from arcos.tools

Installation

The arcos python package can be installed with:

    pip install arcos4py

Credits

Maciej Dobrzynski (https://github.com/dmattek) created the original ARCOS algorithm.

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