ARCOS DOCS
  • ARCOS
  • Algorithm Overview
    • Event Detection and Tracking
    • Data Requirements
    • Preprocessing
  • Installation
    • Avalilable Implementations
      • R package "ARCOS"
      • Python Package "arcos4py"
      • Napari Plugin "arcos-gui"
  • Example Use Cases
    • Detecting Collective signalling events in epithelial Cells
    • Analysing Collective Phenomena in Honeybees
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  • Identification of spatial clusters
  • Flow-Chart overview of ARCOS event detection

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  1. Algorithm Overview

Event Detection and Tracking

How does ARCOS work

PreviousARCOSNextData Requirements

Last updated 11 months ago

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Identification of spatial clusters

A dbscan algorithm first spatially clusters entities deemed active in each frame. In step 2, clusters are sequentially linked between frames to capture collective events over time. The cluster in frame one forms a seed of a collective event. The cluster in frame two is linked to this seed cluster because several of its member cells are within the neighbourhood radius 𝜀. In frame three, only clusters #3, #5, & #6 are linked to the previous frame’s cluster. Cells in cluster #4 are too far and thus form a new seed of a collective event.

Flow-Chart overview of ARCOS event detection

The flow chart below outlines the steps in the algorithm.

Figure 1: Demonstration of the ARCOS algorithm on a growing activity cluster
Figure 2: Step by step overview of the event detection and tracking