TrackMateR

Analysis of TrackMate XML outputs in R.

TrackMate is a single-particle tracking plugin for ImageJ/Fiji. The standard output from a tracking session is in TrackMate XML format.

The goal of this R package is to import all of the data associated with the final filtered tracks in TrackMate for further analysis and visualization in R.

Installation

Once you have installed R and RStudio Desktop, you can install TrackMateR using devtools

# install.packages("devtools")
devtools::install_github("quantixed/TrackMateR")

An Example

A basic example is to load one TrackMate XML file, calibrate it (if needed) and analyse it.

library(ggplot2)
library(TrackMateR)
# an example file is provided, otherwise use file.choose()
xmlPath <- system.file("extdata", "ExampleTrackMateData.xml", package="TrackMateR")
# read the TrackMate XML file into R using
tmObj <- readTrackMateXML(XMLpath = xmlPath)
#> Units are:  1 pixel and 0.07002736 s 
#> Spatial units are in pixels - consider transforming to real units
#> Collecting spot data. Using 20 cores
#> Matching track data...
#> Calculating distances...
# Pixel size is actually 0.04 um and original data was 1 pixel, xyscalar = 0.04
tmObj <- correctTrackMateData(dataList = tmObj, xyscalar = 0.04, xyunit = "um")
#> Correcting XY scale.
# generate a report
reportDataset(tmObj)

TrackMateR can generate several different types of plot individually using commands or it can make them all automatically and create a report for you.

• For details of how to make individual plots and/or tweak the default parameters, see vignette("TrackMateR")
• To see how to compare different datasets, see vignette("comparison")
• In order to rescale or recalibrate TrackMate data, see vignette("recalibration")

Credits

• TrackMateR builds on initial work by Julien Godet on trackR.
• Méghane Sittewelle provided example TrackMate data and helped with testing TrackMateR.
• The Fiji plug-in TrackMate is developed by Jean-Yves Tinevez.

Limitations, future development

• TrackMateR is currently written for 2D data. 3D data is read but analysis is currently on the first two dimensions.
• Addition of a dry run option to quickly report what data would be analysed by compareDatasets().
• Addition of number of tracks per frame and more advanced calculation of the density of tracks.