This repository contains the codes used in the manuscript.
Get git https://git-scm.com/
git clone https://github.com/lgrozinger/pyolin.git
cd pyolin
These dependencies are often found in the official package repositories for nix systems.
- Get Python3.6+ https://www.python.org/downloads/release/python-380/
- Get gnuplot 5+ http://www.gnuplot.info/
- Get texlive https://www.tug.org/texlive/ (or equivalent LaTeX)
- Get imagemagick https://imagemagick.org/index.php
on debian-based systems these dependencies are available from the official package repositories, and can be installed with:
apt-get update && apt-get install python3 python3-pip gnuplot texlive-base texlive-fonts-recommended imagemagick
the required python packages can then be installed with
python3 -m pip install --upgrade pip && python3 -m pip install jupyter pandas scipy matplotlib similaritymeasures seaborn
A Dockerfile is provided for convenience:
Get Docker https://docs.docker.com/engine/docker-overview/
Build once with docker build -t pyolin .
Run with docker run -it -v "$(pwd)":/pyolin pyolin /bin/bash
after cd pyolin, running python3 produce_figures.py full-update will do the analysis from the manuscript and produce the plots found there.
The code can also be used as a python package. See https://github.com/lgrozinger/pyolin/blob/master/notebooks/example.ipynb for example usage.
The preprocessed cytometry data is located at https://github.com/lgrozinger/pyolin/blob/master/standardised_cheeky.csv . This data was processed using the FlowScatt package https://github.com/rstoof/FlowScatt . The analysis in the paper can be performed on other datasets, providing it follows the format in the example dataset, by modifying the variable CYTODATA in __init__.py to point at the filename of the data to be analysed.
SBOL files for the constructs used in the study are found in the results/sbol/ directory with the extension .xml