This repository contains the Python code associated with the following paper:
- Barry DJ, Marcotti S, Gerontogianni L and Kelly G (2025). Enhancing Reproducibility Through Bioimage Analysis: The Significance of Effect Sizes and Controls.
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.
To test our code on your own data, the easiest thing to do is download this repo and run the Nuclear_Fascin.cppipe CellProfiler pipeline on your own images and replace the files in the cell_profiler_outputs folder. You can then use the Jupyter Notebook to generate plots for your own images.
A step-by-step guide 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.
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).
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.