Skip to main content

A python package to detect collective spatio-temporal phenomena.

Project description

arcos4py

pypi conda-forge python Build Status codecov

Arcos4py is a python package to detect collective Spatio-temporal phenomena.

Features

Automated Recognition of Collective Signalling for python (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 and can track events over time. Such collective waves have been recently identified in various biological systems and have been demonstrated to play a crucial 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 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 spatiotemporally correlated phenomena.

Todo's

  • Add additional tests for binarization and de-biasing modules.
  • Add local indicators of spatial autocorrelation (LISA) as a binarization method option.

Data Format

The time series should be arranged in a long table 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

Arcos4py can be installed from PyPI with:

    pip install arcos4py

Napari Plugin

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

arcos_demo

Credits

Maciej Dobrzynski created the original ARCOS algorithm.

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

arcos4py-0.1.5.tar.gz (25.1 kB view details)

Uploaded Source

Built Distribution

arcos4py-0.1.5-py3-none-any.whl (22.0 kB view details)

Uploaded Python 3

File details

Details for the file arcos4py-0.1.5.tar.gz.

File metadata

  • Download URL: arcos4py-0.1.5.tar.gz
  • Upload date:
  • Size: 25.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for arcos4py-0.1.5.tar.gz
Algorithm Hash digest
SHA256 24ef6e44b12f2a9067343a77941117990eb86b0a038b324b70505380f0fe0afb
MD5 013a94087d410b27135f24abd21f2013
BLAKE2b-256 2c8e3b2459f99e7df06b75778dd00c6a8985cbca5d956aa9a3f626d8f2947610

See more details on using hashes here.

File details

Details for the file arcos4py-0.1.5-py3-none-any.whl.

File metadata

  • Download URL: arcos4py-0.1.5-py3-none-any.whl
  • Upload date:
  • Size: 22.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for arcos4py-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 f0fd447bc26eb70a5c6ce4c0479c9cf862085218ec56eeb922d0d2f243c5bfac
MD5 980ecaffbf7e5ff48808cbf336f8af8a
BLAKE2b-256 900f1839d73696394828981f4939f82d34b5b59326bc71bde4ba67ff7c005693

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page