README

Analysis script for radial distances from centroid to boundary of object in 2D

Script developed by Damian Dalle Nogare at the Human Technopole BioImage Analysis Infrastructure Unit, National Facility for Data Handling and Analysis. Licensed under the BSD-3 license.

Jupyter notebooks tested on Red Hat Enterprise Linux 8.6 on a AMD Epyc 7763 processor under Python 3.8.19 using jupyter-core 5.7.2.

Instructions for Installation (typical install time, < 2 mins)

1. Place the scripts single_image_analysis.ipynb, calculate_circle_distances.ipynb and the environment.yml file into a folder.
2. Activate a terminal with conda installed and install the environment and dependencies using conda env create -f environment.yml
3. Activate the environment by typing conda activate chabot_2024
4. Launch a jupyter instance by typing jupyter notebook

Instructions for use (typical runtime on test data, 2.5 seconds, not counting image loading time)

1. Download the example data from https://tinyurl.com/chabot2024testdata and place the two files (test_raw.ims and test_tracked.tif in a folder. Note that this data will be moved after publication to a more stable repository (ie the bioimage archive).
2. In a jupyter notebook, open the single_image_analysis.ipynb notebook.
3. Execute the first two cells to load the dependencies (1) and initialize functions (2)
4. Update the raw_image and tracked_image variables to contain the path to the raw image and tracked image respectively (downloaded above).
5. Run the analysis by executing the line df = process_single_image(tracked_image, raw_image). The results will be stored in the resulting dataframe
6. The output of this script on the supplied test data should reproduce the results in test_output.csv

Running the software on your data

This software was written to operate on the data generated for the current publication. In order to run this analysis on your own data, you must first ensure that the data is formatted in a manner consistent with the test data. In this case, you need two files

1. A tracked label file, in the shape TZYX containing 3D segmented labels for each locus
2. A raw data file, in the shape TCZYX, containing the raw data. In this case, it is important that the 6th channel contains the segmented (binary) RNA transcription signal which will determine when transcription is initiated
3. Run the script, updating where appropriate the file paths

Calculate Circle Distances (control script)

1. Activate the environment installed above by typing conda activate chabot_2024
2. Open the script calculate_circle_distances.ipynb in a jupyter session.
3. Run the first two cells to load the dependencies and define the functions
4. Run the third cell to generate the control data.
5. The output will be a folder called circles in the current working directory which contains, for each circle diameter of 2, 3, 4 and 5 pixels, a tif file containing the raw image and a csv file containing the results of the analysis.
6. As a control, the results for radius 5 are supplied as radius_5.csv. Confirm that the results of this script match the supplied results.