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

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

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

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

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

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

What is dagitty | List testable implications of a structural equation model | List adjustment sets for specific path coefficients | List path coefficients that are identifiable by regression | List adjustment sets for specific total effects | List total effects that are identifiable by regression | List path coefficients that are identifiable through instrumental variables