oral-cavity-paper
This repository can be used to reproduce and/or update the tables and figures of our publication titled "Patterns of Lymph Node Involvement for Oral Cavity Squamous Cell Carcinoma".
Science Score: 44.0%
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Low similarity (16.4%) to scientific vocabulary
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Repository
This repository can be used to reproduce and/or update the tables and figures of our publication titled "Patterns of Lymph Node Involvement for Oral Cavity Squamous Cell Carcinoma".
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README.md
Patterns of Lymph Node Involvement for Oral Cavity Squamous Cell Carcinoma
This repository provides the source code to reproduce all figures and table in our publication with the above title that we intend to submit to The Green Journal and update them in case the data changes. Below we provide instructions on how to achieve this.
[!WARNING] This paper is still under review and not yet published. Content and data here are still subject to change.
[!IMPORTANT] All software in this repository is licensed under the MIT license. However, when executed, the pipeline described here will download data that is licensed under CC BY-SA 4.0.
Reproduce the Pipeline
First, clone the repository:
git clone https://github.com/rmnldwg/oral-cavity-paper
And change your working directory to the root of the repository:
cd oral-cavity-paper
Using bash
[!NOTE] Requires bash on Linux
If you're using bash on Linux and don't want to install Docker, you may get away with simply running the repro.sh script:
bash setup.sh
It should do the following things in order:
1. pull changes from the GitHub remote repo,
2. automatically find your latest Python 3 installation,
3. set up a virtual environment,
4. install all Python packages defined in requirements.txt,
5. download (and update) the data sources,
6. reproduce the pipeline
But this script is probably quite platform dependent. So, if this does not work, a safer way to get the desired results is using Docker, which is explained below.
Using Docker
[!NOTE] Requires a Docker installation.
Although Docker is platform independent, it is sometimes a hassle to get it installed and running. Here is a guide to the Docker installation.
If Docker is up and running on your system, the following two commands should do the reproduction:
docker build -t ocp-image .
and then run it with the following command:
docker run \
--rm --volume .:/usr/src/oral-cavity-paper \
--name ocp-container \
ocp-image
That should run the pipeline from joining the datasets to plotting the figures and tables.
Owner
- Name: Roman Ludwig
- Login: rmnldwg
- Kind: user
- Location: Zurich
- Company: University Hospital Zurich
- Twitter: rmnldwg
- Repositories: 6
- Profile: https://github.com/rmnldwg
PhD student in the department of physics at UZH. Working on probabilistic models for lymphatic tumor progression and initiating data sharing projects.
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: Pipeline for Oral Cavity Paper
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Roman
family-names: Ludwig
email: roman.ludwig@usz.ch
affiliation: University Hospital Zurich
orcid: 'https://orcid.org/0000-0001-9434-328X'
- given-names: Lars
family-names: Widmer
affiliation: University of Zurich
repository-code: 'https://github.com/rmnldwg/oral-cavity-paper'
abstract: >-
This software can be used to reproduce and/or update the
tables and figures of our publication titled "Patterns of
Lymph Node Involvement for Oral Cavity Squamous Cell
Carcinoma".
keywords:
- cancer
- oral cavity
- lymphatic spread
- metastasis
- reproducible research
license: MIT
date-released: '2023-11-24'
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Dependencies
- python 3.10-slim-bullseye build
- black *
- dvc *
- lyscripts *
- matplotlib *
- matplotlib-venn *
- numpy *
- pre-commit *
- pylint *
- scikit-learn *
- scipy *
- seaborn *
- statsmodels *
- black *
- dvc *
- lyscripts *
- matplotlib *
- matplotlib-venn *
- numpy *
- pre-commit *
- pylint *
- scikit-learn *
- scipy *
- seaborn *
- statsmodels *