celltrackR {celltrackR}

celltrackR: Quantitative analysis of motion.

Description

The CelltrackR package is designed for analyzing cell tracks acquired by time-lapse microscopy (like those provided in the included datasets TCells, BCells and Neutrophils). But it can of course process any x-y-(z)-t data, and we hope that it may be useful for other purposes as well.

Details

For a complete list of functions, use help( package="celltrackR" ). A handy cheat sheet is available in pdf. You can open it by calling the function cheatsheet.

Data structure

The basic data structure that most functions in this package operate on is a set of tracks. A track is a list of spatial coordinates that are recorded at fixed time intervals; the function timeStep can be used to check for fluctuations of the recording intervals.

We expect tracks to be stored in a matrix (or data frame, but this is discouraged for efficiency reasons) whose first column denotes a time interval (e.g. seconds elapsed since the beginning of the experiment), and whose remaining columns denote a spatial coordinate. A set of tracks is stored as a list with S3 class tracks. CelltrackR provides some S3 methods for this class, which are explained in tracks as well as plot.tracks, sort.tracks and as.list.tracks.

Track analysis in celltrackR

A wide range of common track measures are included in the package. These are all functions that take a single track as an input, and output one or several numbers. For instance, the function speed estimates the average instantaneous speed of the track by linear interpolation, and straightness computes the start-to-end distance divided by the trajectory length (a number between 0 and 1, where 1 indicates a perfectly straight track). See TrackMeasures for an overview of measures that can be analyzed on tracks. Also see AngleAnalysis for an overview of functions that can help detect directional bias and tracking artefacts (see Beltman et al, 2009).

CelltrackR is designed to support various flavors of track analysis that have been suggested in the literature. The simplest kind is a track-based analysis, where we compute a single statistic for each track in a dataset (Beltman et al, 2009). Because track sets are lists, this is achieved simply by using lapply or sapply together with the track measure (see Examples).

In step-based analyses (Beltman et al, 2009), we chop each track up into segments of the same length and then apply our measures to those segments. This can help to avoid biases that arise from variations in track length (which are always present in cell tracking experiments). In CelltrackR, step-based analyses are performed by using the subtracks function. Often we want to perform such step-based analyses for all possible subtrack lengths simultaneously, and plot the result as a function of the subtrack length; a famous example is the mean square displacement plot. This can be achieved by using the aggregate.tracks function, which has options to control which subtrack lengths are considered and whether overlapping subtracks are considered.

In a staggered staggered analysis (Mokhtari et al, 2013), we analyse all subtracks (of any length) of a single track, and typically plot the result as a matrix. This can reveal dynamic patterns along a single track, e.g. turning behaviour or local slowdowns. Staggered analyses can be performed using the applyStaggered function.

Simulating tracks in celltrackR

Lastly, in addition to data analysis, the package contains some function to generate cell tracks by simulation. This is useful to develop and benchmark track analysis methodology (Textor et al, 2011), and for computational biology studies that try to extrapolate the long-term consequences of observed cell migration behaviour. Alongside a simple uncorrelated random walk (brownianTrack), this package implements a simulation model proposed by Beauchemin et al (2007) in the function beaucheminTrack. That model can also simulate directionally biased motion.

Author(s)

Johannes Textor, Katharina Dannenberg, Jeffrey Berry, Gerhard Burger, Inge Wortel Maintainer: Johannes Textor <[e-mail]>

References

Joost B. Beltman, Athanasius F.M. Maree and Rob. J. de Boer (2009), Analysing immune cell migration. Nature Reviews Immunology 9, 789–798. doi:10.1038/nri2638

Zeinab Mokhtari, Franziska Mech, Carolin Zitzmann, Mike Hasenberg, Matthias Gunzer and Marc Thilo Figge (2013), Automated Characterization and Parameter–Free Classification of Cell Tracks Based on Local Migration Behavior. PLoS ONE 8(12), e80808. doi:10.1371/journal.pone.0080808

Johannes Textor, Antonio Peixoto, Sarah E. Henrickson, Mathieu Sinn, Ulrich H. von Andrian and Juergen Westermann (2011), Defining the Quantitative Limits of Intravital Two-Photon Lymphocyte Tracking. PNAS 108(30):12401–12406. doi:10.1073/pnas.1102288108

Catherine Beauchemin, Narendra M. Dixit and Alan S. Perelson (2007), Characterizing T cell movement within lymph nodes in the absence of antigen. Journal of Immunology 178(9), 5505-5512. doi:10.4049/jimmunol.178.9.5505

See Also

The package vignettes, available from browseVignettes( package="celltrackR" ). Make sure you have installed the package with option build_vignettes = TRUE, or vignettes will not be visible. Also check out the package cheat sheet, which is available by calling the function cheatsheet.

Examples

  ## track-based speed comparison
  boxplot(sapply( Neutrophils, straightness ), sapply( BCells, straightness ))

  ## step-based turning angle comparison
  boxplot(sapply(subtracks(Neutrophils, 2), overallAngle),
    sapply(subtracks(BCells, 2), overallAngle))

 ## mean square displacement plot; a step-based displacement analysis for all step lengths
 plot(aggregate(TCells, squareDisplacement)[,"value"])

 ## 'staggered' analysis of displacement over whole track. Reveals that this track
 ## slows down near its beginning and near its end.
 filled.contour(applyStaggered(TCells[[4]], displacement, matrix=TRUE))

 ## a simple hierarchical clustering based on 2D asphericity

 ## tag track IDs so we can identify them later
 names(TCells) <- paste0("T",names(TCells))
 names(BCells) <- paste0("B",names(BCells))
 names(Neutrophils) <- paste0("N",names(Neutrophils))
 ## project all tracks down to 2D
 cells <-  projectDimensions(c(TCells,BCells,Neutrophils), c("x","y"))

 ## compute asphericity
 asph <- lapply(cells, asphericity)

 ## plot clustering
 plot(hclust(dist(asph)))