Package: track-analyzer
Track Analyzer :microscope: :bar_chart:Quantification and visualization of tracking dataTrack Analyzeris Python-based data visualization pipeline for tracking data.
Itdoes notperform any tracking, but visualizes and quantifies any kind of tracked data.
It analyzes trajectories by computing standard quantities such as velocity,
acceleration, diffusion coefficient, local density, etc.
Trajectories can also be plotted on the original image in 2D or 3D using custom color-coding.Track Analyzerprovides a filtering section that can extract subsets of data based on spatiotemporal criteria.
The filtered subsets can then be analyzed either independently or compared. This filtering section also provides a tool
for selecting specific trajectories based on spatiotemporal criteria which can be useful to perform fate mapping and back-tracking.Track Analyzeris distributed as two versions: aninstallation-free web-based interfacerun onGalaxy, and a full version that has to be run on a local computer. On both versions,Track Analyzercan be run without any programming knowledge using its graphical interface. The full version interface is launched by running aJupyter notebookcontaining widgets allowing the user to load data and set parameters without writing any code.Data requirementsThe mandatory input file ofTrack Analyzeris a data table (a csv or txt file) of tracks containing the position coordinates (in 2D or 3D) along time and the tracks identifiers.
Optionally, data can be plotted on the original image provided as a 3D or 4D tiff stack (ie. 2D+time or 3D+time). If the format of your movie is
different (list of images), please convert it to tiff stack usingFijifor instance.The position file must contain columns with the x, y, (z) positions, a frame column and track id column. The positions coordinates can be in
pixels or in scaled data. The information about the scaling and other metadata such as time and length scales will be provided by the user through the graphical interface.Full version installationTo benefit from all the functionalities (especially 3D rendering and hand-drawing selection), you need to install the full version locally on your computer.
It is fully written in Python, so you just need to have Python running on your computer. If you haven't installed Python yet, install Python 3.10. For instance, just downloadminiconda 3.10. Then you can installTrack Analyzerin a clean environment using conda.To do so, run on a Terminal (Mac & Linux) or an Anaconda powershell (Windows):condacreate-y-nTA_env-cconda-forgepython=3.10condaactivateTA_envTrack Analyzerdepends onnapari, whose installation can be tricky. For this reason, we recommend to install it independently using conda before installingTrack Analyzerusing pip.condainstall-cconda-forgenaparipipinstalltrack-analyzerNext, you can next runTrack Analyzerusing ourJupyter notebook. Don't forget to first activate the environment:conda activate TA_env.Installation-free versionThe installation-free online version is availablehere. It is run on the web-based platformGalaxy, which is easy to use (some documentation regarding Galaxy is availablehere). A quickstart tutorial to Galaxy's interface is presented in Track Analyzer'sdocumentation.DocumentationYou can find a complete documentationhere.TroubleshootingThe 3D visualization and the drawing selection tool depend on thenaparipackage.
The installation of this package can lead to issues depending on your system.
If you are not able to solve this installation, you will not be able to have access to 3D rendering. However, you will still be able to useTrack Analyzerwithout the drawing tool, by using coordinates sliders in the graphical interface.The execution of blocks in the Jupyter notebook can be buggy because of the large number of widgets. If you can't normally execute a block by pressing Shift+Enter, use the Execute button at the top of the notebook.