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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  1. Algorithm Overview

Data Requirements

PreviousEvent Detection and TrackingNextPreprocessing

Last updated 2 years ago

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Arcos is aimed at analysing time series data which should be arranged in . Each row should define the object’s location, time, and optionally the measurement value. For the implementations in R and python, the objects don't necessarily need to be tracked over time. However, it is recommended as it allows a more in-depth downstream analysis of ARCOS's output.

An example dataset could look like this:

Index
t
x
y
m
id
Position

0

1

0.22

-0.15

0

1

0

1

1

0.88

-0.11

0

2

0

2

1

1.93

0.07

0

3

0

3

1

2.95

0.18

0

4

0

4

1

3.90

-0.04

0

5

0

For the napari-plugin, as of now, a track id is required.

long format