napatrackmater 3.0.7

Import Trackmate XML files for Track Visualization and analysis in Napari.

NapaTrackMater

Napari Visualization tool for Trackmate > 6.0 and bTrackmate XML files for 3D + time tracks.

This repository is the bridge between the Fiji and Napari world for exporting and viewing the track XML files using Napari track layer.

Installation

This package can be installed with:

pip install --user napatrackmater

If you are building this from the source, clone the repository and install via

git clone https://github.com/kapoorlab/NapaTrackMater/

cd NapaTrackMater

pip install --user -e .

# or, to install in editable mode AND grab all of the developer tools
# (this is required if you want to contribute code back to NapaTrackMater)
pip install --user -r requirements.txt

Pipenv install

Pipenv allows you to install dependencies in a virtual environment.

# install pipenv if you don't already have it installed
pip install --user pipenv

# clone the repository and sync the dependencies
git clone https://github.com/kapoorlab/NapaTrackMater/
cd NapaTrackMater
pipenv sync

# make the current package available
pipenv run python setup.py develop

# you can run the example notebooks by starting the jupyter notebook inside the virtual env
pipenv run jupyter notebook

Access the example folder and run the cells.

Usage

To use this repository you need to have an XML file coming either from the Fiji plugin Trackmate version>6.0 or from bTrackmate version>2.0. Both the programs save the same XML file containing the information about your tracking session. This XML file can be used to re-generate trackscheme along with the tracks they came from, for example a typical trackscheme with tracks overlay in Fiji:

In this scheme there are some cells that divide multiple times and some don't, we can view these tracks by using the tracks layer of Napari.

In Napari tracks layer view we break the dividing trajectory into components that are called tracklets. These tracklets represent the trajectory of individual daughter cell or the root cell.

More than just viewing the tracks we can extract the following special functions from them:

1. If the cells move inside a tissue we can calculate the distance of the cells in a track from the tissue boundary for the root track and the following tracklets after a division event, this gives a cell localization plot which shows the starting and the ending distance of each tracklet. Check the notebook.

2. If the cells had an intensity oscillation we can compute the frequency of such oscillation for each tracklet of the track by Fourier transforming the intensity over time. Check the notebook.

Example

To try the provided notebooks we provide an example dataset of C. elegans from the tracking challenge, download the zip file of hyperstacks of the Raw, segmentation and the mask image from here. The zip file inflates inside a data folder that you can extract inside the examples folder. In the output folder we created the csv file using this notebook. After that we used the jar provided in this repo and installed it in Fiji to do the tracking of 12000 cells in 3D (after removing cells below a certain size) and kept 54 tracks. In the tracking process we automatically exculde the tracklets whose track duration is less than 10 timeframes. After that we save the xml file of the tracks. This xml file can be opened for track visualization and analysis using the notebooks provided in the example/ folder.

Now you can try either of the two notebooks provided:

Notebook 1 ) To view the intensity oscillations of the cells, click on this notebook. We separate the visualization and analysis of dividing and non-dividing trajectories, in the cell of dividing trajectories you will see the Napari tracks layer with their ID for all the dividing tracks only along with the image, segmentation and mask layers. To view the tracks you can use the time slider to see the tracks alongside the cells. On the left panel we have the image and tracks layer whose display properties can be changed from the top left panel (1). In the bottom left we have the dropdown menu enlisting all the tracks (2). Users can select the track to be displayed in the central window where it is easy to switch between the hyperstack and the 3D view (3). The user can also choose to view all the tracks at once and then toggle the visibilty of the tracks using the eye icon next to the image and tracks layer (4). On the top right panel we show two plots displaying the track information (5). The 3D central view can be rotated, translated and the selected view can be saved as an animation using the bottom right animation panel (6). For the cells that divide we show the intensity variation and associated fast fourier transform for each tracklet.

Example of publication where such oscillations in intensity were found: Collaboration: Ines Lahmann, Varun Kapoor, Stephan Preibisch

Notebook 2) To view the cell localization in a track with respect to the tissue boundary click on this notebook Again we divide the notebook into analysis of dividing and non-dividing trajectories. In the plots we now show the distance of the cells in the track to the boundary and plot the starting end distance of the parent (in green) and the daughter cells (in red).

Docker

A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with:

docker build -t kapoorlab/NapaTrackMater .

Now to run the track command:

# show help
docker run --rm -it kapoorlab/NapaTrackMater
# run it with example data
docker run --rm -it -v $(pwd)/examples/data:/input kapoorlab/NapaTrackMater track -f /input -r /input/Raw.tif -s /input/Seg.tif -s /input/Mask.tif -n test

Requirements

• Python 3.9 and above.

License

Under MIT license. See LICENSE.

Authors

• Varun Kapoor [e-mail]
• Claudia Carabaña
• Mari Tolonen