Introduction | Dataset | 1 Track length | 1.1 Finding the length of tracks in the dataset | 1.2 Dealing with short tracks | 2 Detecting and correcting drift | 2.1 Adding artificial tissue drift to the Tcell data | 2.2 Detecting global directionality: hotellingsTest | 2.3 Detecting global directionality: angle analysis | 2.4 Correcting drift | 3 Detecting artifacts using angle analyses | 3.1 Detecting double tracking: angle versus distance between cell pairs | 3.2 Detecting tracking errors near border or imprecise z-calibration: distances and angles to border planes | 4 Detecting and correcting variation in time resolution | 4.1 Detecting variation in timesteps | 4.2 Example: detecting missing data in tracks | 4.3 Correcting gaps or variation in timesteps | 4.4 Comparing experiments with a different time resolution | 5 Detecting non-motile cells | 5.1 Option 1: filtering based on track measures such as speed | 5.2 Option 2: modelling coordinates as multivariate Gaussian
Package: celltrackR 1.2.2
celltrackR: Motion Trajectory Analysis
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) <doi:10.4049/jimmunol.178.9.5505>, Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>, Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>, Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>, Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>, Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>, Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>, Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.
Authors:
celltrackR_1.2.2.tar.gz
celltrackR_1.2.2.zip(r-4.7)celltrackR_1.2.2.zip(r-4.6)celltrackR_1.2.2.zip(r-4.5)
celltrackR_1.2.2.tgz(r-4.6-any)celltrackR_1.2.2.tgz(r-4.5-any)
celltrackR_1.2.2.tar.gz(r-4.7-any)celltrackR_1.2.2.tar.gz(r-4.6-any)
celltrackR_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
celltrackR/json (API)
| # Install 'celltrackR' in R: |
| install.packages('celltrackR', repos = c('https://jtextor.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- BCells - Two-Photon Data: B Cells in a Lymph Node
- Neutrophils - Two-Photon Data: Neutrophils responding to an infection in the ear
- TCells - Two-Photon Data: T Cells in a Lymph Node
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:379bad2db5. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 187 | ||
| source / vignettes | OK | 279 | ||
| linux-release-x86_64 | OK | 175 | ||
| macos-release-arm64 | OK | 180 | ||
| macos-oldrel-arm64 | OK | 184 | ||
| windows-devel | OK | 142 | ||
| windows-release | OK | 129 | ||
| windows-oldrel | OK | 136 | ||
| wasm-release | OK | 130 |
Exports:analyzeCellPairsanalyzeStepPairsangleCellsangleStepsangleToDirangleToPlaneangleToPointapplyStaggeredas.tracksasphericitybeaucheminTrackbootstrapTrackboundingBoxbrownianTrackcellPairscheatsheetclusterTracksdisplacementdisplacementRatiodisplacementVectordistanceCellsdistanceStepsdistanceToPlanedistanceToPointdurationfilterTracksfractalDimensionget.immap.metadataget.immap.tracksgetFeatureMatrixhotellingsTesthurstExponentinterpolateTrackis.tracksmaxDisplacementmaxTrackLengthmeanTurningAnglenormalizeToDurationnormalizeTracksoutreachRatiooverallAngleoverallDotoverallNormDotpairsByTimeparse.immap.jsonplot3dplotTrackMeasuresprefixesprojectDimensionsread.immap.jsonread.tracks.csvrepairGapsselectStepsselectTrackssimulateTracksspeedsplitTracksquareDisplacementstaggeredstepPairsstraightnesssubsamplesubtrackssubtracksByTimetimePointstimeSteptrackFeatureMaptrackLengthtracksvecAnglewrapTrack
Quality Control and Preprocessing
Last update: 2025-09-19
Started: 2020-03-31
Clustering Tracks with CelltrackR
Introduction | Datasets | 1 Extracting a feature matrix | 2 Dimensionality reduction methods: PCA, MDS, and UMAP | 1.1 PCA | 1.2 MDS | 1.3 UMAP | 3 Clustering: hierarchical clustering and k-means | 3.1 Hierarchical clustering | 3.2 K-means clustering
Last update: 2024-03-27
Started: 2020-03-31
Quality Control and Preprocessing of the Datasets in the Package
Introduction | Before we start | 1 Short tracks | 2 Drift? | 3 Border artifacts? | 4 Non-motile cells? | 5 Double tracking? | 6.6 Gap correction | 6.7 Time resolution
Last update: 2022-03-21
Started: 2022-03-21
Reading, Converting, and Filtering Tracking Data
Introduction | 1 Reading in data | 1.1 Input data format | 1.2 Directly reading in data as a tracks object | 2 The tracks object | 2.1 The tracks object data structure | 2.2 Subsetting data | 2.3 Using tracks objects in combination with R's lapply and sapply | 2.4 Built-in filtering/subsetting functions | 2.5 Extracting subtracks | 3 Converting between tracks objects and other data structures
Last update: 2022-03-21
Started: 2020-03-31
Simulating Tracks
Introduction | Datasets | 1 Modelling brownian motion | 1.1 A simple random walk | 1.2 Matching displacement to data | 1.3 A biased random walk | 2 The Beauchemin model of lymphocyte migration | 3 Bootstrapping method for simulating migration | 4 Example: Comparing data with models | 4.1 Mean square displacement plot | 4.2 Persistence: autocovariance plot | 5 Fitting models on the MSD | 5.1 Before we start: dimensionality and imaging windows | 5.2 Fitting Brownian motion based on the diffusion coefficient | 5.3 Fitting the Beauchemin model
Last update: 2022-03-21
Started: 2020-03-31
Track Analysis Methods
Introduction | Datasets | 1 Simple track visualization | 1.1 2D and 3D plotting | 1.2 Star plots (Rose plots) | 2 Quantification of measures on tracks | 2.1 Cell-based analyses | 2.2 Step-based analyses | 2.3 Staggered analyses | 3 Mean square displacement plots | 4 Analyzing persistence | 4.1 Straightness metrics | 4.2 Autocorrelation plots | 5 Analyzing directionality | 5.1 Hotelling's test | 5.2 Angle analyses | 5.2.1 Overview: Angles and distances of steps to a fixed reference (point, plane, or direction) | 5.2.2 Angles and distances to planes: detecting tracking artefacts | 5.2.3 Angle to a reference direction: powerful directionality tests when direction is known | 5.2.4 Angles and distances to reference point: detecting movement towards a single point | 5.2.5 Angles and distances between pairs of cells or individual steps
Last update: 2022-03-21
Started: 2020-03-31
