Nucleoid distance on mitochondria

This code was developed by the EPFL Center for Imaging for the Laboratory of Experimental Biophysics.

Installation

Step 1: Create and Activate a Virtual Environment

Using venv:\

python3 -m venv mito
source mito/bin/activate

Using conda:\

conda create -n mito
conda activate mito
conda install pip

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Run the Code

python main.py

Parameters to Tune and Variables to Change

Scroll down to the __main__ section of main.py. Note that the names of variables containing coordinates always end in _um for micrometers or _px for pixels.

pixel_size_um

Description: Adjust this if your data has a different pixel size.
Format: The values should be the voxel size in micrometers, in the order z, y, x.

../test_folder

Description: Specify the folder containing your experiments.
Format: Each experiment should have a separate folder, with each folder containing a file named <folder_name>_decon.ome.tiff.

min_length_px

Description: Segments shorter than this value will be discarded.

knots2data_ratio

Description: The ratio between the number of knots for the spline and the number of data points/pixels for a track.
Effect: Larger values result in smoother approximations, while smaller values more closely follow the original segmentation.

extension_um

Description: The amount by which the splines are extended at the ends.
Effect: Capture nucleoids near the ends of mitochondria.

half_window_size_um

Description: Half the size of the normal plane images that are extracted.
Effect: Larger values will capture nucleoids further away.

scipy.signal.find_peaks

Description: All parameters of this function can be adjusted to find the peaks corresponding to nucleoids.