This repository contains the Python code associated with the following paper:
- Barry DJ, Marcotti S, Gerontogianni L and Kelly G (2025). A Statistical Framework for Robust and Reproducible BioImage Analysis. https://doi.org/10.1101/2025.02.10.637409
The quickest and easiest way to try this code is to try it on Binder. This will allow you to reproduce the plots in the associated publication. On some occasions, you may find Binder produces the following error:
Should this occur, simply close the tab and relaunch binder from the link above.
You can modify the Jupyter notebooks to run on your own data. In order to do this, you will need to produce some data to analyse - for this you have two options.
Download this repo and run the Nuclear_Localisation CellProfiler pipeline on your own images and replace the files in the cell_profiler_outputs folder. You can then use the Jupyter Notebooks companion_notebook_idr0028.ipynb or companion_notebook_idr0139.ipynb to generate plots for your own images. Both these notebooks produce similar outputs, but they have been configured to handle slightly different input data formats, specific to the requirements of the IDR0028 and IDR0139 datasets.
You can analyse your images using any software that outputs the results of the analysis in a CSV file. Then, download this repo and use the CSV file as input for the companion_framework_notebook.ipynb notebook.
A step-by-step guide to downloading the repo and running the notebooks is presented below. You only need to perform steps 1 and 2 once. Every subsequent time you want to run the code, skip straight to step 3.
Click on the small arrow on the green Code button above and then click Download Zip:
When the download completes, unzip the contents. You should now have a folder that looks like this:
Below, we will use the requirements file to set up a python environment to run the Jupyter notebooks contained in the notebooks folder.
If you are already familiar with Git, you can obviously clone this repo like any other. However, some of the data in the inputs folder is quite large. As such, you will need to install Git LFS to download the full dataset.
We recommend using conda as it's relatively straightforward and makes the management of different Python environments simple. You can install conda from here (miniconda will suffice).
The notebooks in this repository will only work if your own data is stuctured appopriately. If you wish to run the Nuclear_Localisation CellProfiler pipeline on your own images, the outputs must be structured in the same way as the inputs in this repository. This assumes that the raw data has originated from the Image Data Resource and has a suitable annotations file associated with (like this one, for example). Your data can then be analysed using either companion_notebook_idr0028.ipynb or companion_notebook_idr0139.ipynb. It is certainly possible to adapt the notebooks to analyse data from other sources, but a reasonable knowledge of Python coding would be required to achieve this.
Alternatively, you can analyse your images using any software that outputs the results of the analysis in a CSV file. Then, download this repo and use the CSV file as input for the companion_framework_notebook.ipynb notebook. Ensure that your CSV file contains data in the tidy format described by Pylvänäinen et al (2025):
Once conda is installed, open Anaconda Prompt and run the following series of commands:
conda create --name enhancing-reproducibility pip
conda activate enhancing-reproducibility
python -m pip install -r <path to this repo>/requirements.txt
where you need to replace <path to this repo> with the location on your file system where you downloaded this repo. You will be presented with a list of packages to be downloaded and installed. The following prompt will appear:
Proceed ([y]/n)?
Hit Enter and all necessary packages will be downloaded and installed - this may take some time. When complete, you can deactivate the environment you have created with the following command.
conda deactivate
You have successfully set up the necessary conda environment!
The following commands will launch a Jupyter notebook allowing you to run the code on your own data:
conda activate enhancing-reproducibility
jupyter notebook <path to this repo>/notebooks/companion_notebook.ipynb
The Jupyter Notebook should open in your browser - follow the step-by-step instructions in the notebook to run the code. If you are not familiar with Jupyter Notebooks, you can find a detailed introduction here.
