This repository hosts code that can be used to analyse the trajectories of animals be means of a semi-supervised clustering algorithm.
For more details of the analysis procedure, as applied to trajectories in the
Morris Water Maze, please refer to Gehring, T.V. et al., Scientific Reports, 2015. The data used in this article is also provided here as an example application of the code (data/mwm_peripubertal_stress folder).
Please note that, althogh this code was initialy applied to trajectories in the Morris Water Maze, the method is general enough as to be applied to other types of experiment.
For the latest version of the code and the data used in the Morris Water Maze
experiments in the above publication please check: https://bitbucket.org/tiagogehring/mwm_trajectories
Using the code:
To use the code a configuration file has to be provided. This configuration is merelly a global object containing some variables and methods which is then referenced by various parts of the code. The configuration file is also used to define the data loading function, segmentation methods of the swimming paths and features used in during the clustering procedure. See config/morris_water_maze/config_mwm.m for an example of such a file (this is the configuration used for the analyzes in the aforementioned publication).
Before running any routines provided here please call the initialize function first. This function has to also be changed accorfingly with the desired configuration file that is to be used.
Following main functionallity is provided in this repository:
A graphical user interface (GUI) for browsing and tagging trajectories or segments of trajectories (gui/browse_trajectories.m). From the GUI a secondary window providing multiple data visualizations (such as individual feature values and clusters) can be accessed. The GUI can also start the semi-supervised clustering algorithm used to classify similar trajectories/segments;
Semi-supervided clustering algorithm (semisupervised_clustering.m): this class is a frontend for the MPCKmeans semi-supervised algorithm. It uses manually labelled data (provided as mapping from trajectory segments to of one or more behavioural classes) to define must-link and cannot-link constracints. Again, for more details see the reference above.
Plotting routines for plotting trajectories (plot_trajectory.m) or the classification results in the form of color bars (one color for each behavioural class) for each trajectory (plot_distribution_strategies.m);
Various other functions for analyzing and validating the clustering results. These are spread over a set of classes (e.g. trajectories, connfusion_matrix, clsutering_resuls). See the results/mwm folder for examples on how to use those functions (the functions in this folder were used to generate the results and figures used in the publication referenced above which compared the behaviour of stressed and non-stressed rats in the MWM).