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celltrackR (version 1.2.2)

Motion Trajectory Analysis

Description

Methods for analyzing (cell) motion in two or three dimensions. Available measures include displacement, confinement ratio, autocorrelation, straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks, steps, or subtracks with varying length. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles. Some of the methodology implemented in this packages was described by: Beauchemin, Dixit, and Perelson (2007) , Beltman, Maree, and de Boer (2009) , Gneiting and Schlather (2004) , Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) , Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) , Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) , Textor, Sinn, and de Boer (2013) , Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) .

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Version

Install

install.packages('celltrackR')

Monthly Downloads

263

Version

1.2.2

License

GPL-2

Maintainer

Johannes Textor

Last Published

September 19th, 2025

Functions in celltrackR (1.2.2)

analyzeCellPairs

Find Distances and Angles for all Pairs of Tracks

Angle between Two Tracks

Read tracks from ImmuneMap

Minimum Distance between Two Cells

Two-Photon Data: T Cells in a Lymph Node

Two-Photon Data: B Cells in a Lymph Node

Distance between Two Steps

as.data.frame.tracks

Convert Tracks to Data Frame

Compute a Measure on a Track in a Staggered Fashion

Angle between Two Steps

interpolateTrack

Interpolate Track Positions

Length of Longest Track

beaucheminTrack

Simulate a 3D Cell Track Using the Beauchemin Model

Get Single Steps Starting at a Specific Time from a Subset of Tracks

Select Tracks by Measure Values

as.tracks.data.frame

Convert from Data Frame to Tracks

Convert from Tracks to List

Find Pairs of Tracks

celltrackR: Quantitative analysis of motion.

Simulate Tracks via Bootstrapping of Speed and Turning Angle from a Real Track Dataset

Simulate an Uncorrelated Random Walk

normalizeToDuration

Normalize a Measure to Track Duration

Bounding Box of a Tracks Object

Decompose Track(s) into Subtracks

subtracksByTime

Extract Subtracks Starting at a Specific Time

Test Unbiasedness of Motion

projectDimensions

Extract Spatial Dimensions

Get Track Prefixes

getFeatureMatrix

Obtaining A Feature Matrix

Open the package cheat sheet

Compute Time Step of Tracks

Find All Unique Time Points in a Track Dataset

normalizeTracks

Normalize Tracks

Process Tracks Containing Gaps

Split Track into Multiple Tracks

read.tracks.csv

Read Tracks from Text File

Angle with a Reference Direction

Plot Tracks in 3D

plotTrackMeasures

Bivariate Scatterplot of Track Measures

Angle with a Reference Plane

distanceToPlane

Distance to a Reference Plane

distanceToPoint

Distance to a Reference Point

Angle with a Reference Point

Generate Tracks by Simulation

get.immap.metadata

Get Track Metadata from ImmuneMap

Distance between pairs of tracks at every timepoint

Sort Track Positions by Time

Plot Tracks in 2D

trackFeatureMap

Dimensionality Reduction on Track Features

Find Pairs of Steps Occurring at the Same Time

Subsample Track by Constant Factor

Angle Between Two Vectors

Staggered Version of a Function

Create Track Object from Single Track

analyzeStepPairs

Find Distances and Angles for all Pairs of Steps

Two-Photon Data: Neutrophils responding to an infection in the ear

aggregate.tracks

Compute Summary Statistics of Subtracks