https://github.com/cbg-ethz/pyggdrasil
Inference and analysis of mutation trees in Python
Science Score: 33.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: pubmed.ncbi, ncbi.nlm.nih.gov -
✓Committers with academic emails
1 of 2 committers (50.0%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.3%) to scientific vocabulary
Repository
Inference and analysis of mutation trees in Python
Basic Info
- Host: GitHub
- Owner: cbg-ethz
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://cbg-ethz.github.io/PYggdrasil/
- Size: 1.14 MB
Statistics
- Stars: 6
- Watchers: 2
- Forks: 0
- Open Issues: 4
- Releases: 1
Metadata Files
README.md
PYggdrasil
Python package for inference and analysis of mutation trees.
PYggdrasil implements the Single Cell Inference of Tumor Evolution (SCITE) algorithm by Kuipers J et al. (2015).
It was designed to quantify the MCMC exploration of tumour progression tree spaces, in particular to investigate Initialisation Strategies, Convergence Diagnostics, & Multi-modalities.
Usage
python
import pyggdrasil as yg
Contributing
Setting up the repository
We recommend creating a Conda environment for isolation:
bash
$ conda create -n pyggdrasil python=3.10
$ conda activate pyggdrasil
and to a simple install with
$ pip install -e .
If you just want to rerun workflows just:
bash
$ poetry install
Quick Run: to run an example workflow, further install snakemake and run a simple MCMC
bash
$ conda install snakemake
$ cd workflows
$ snakemake -c 2 mark00
To build the package and maintain dependencies, we use Poetry. In particular, it's good to install it and become familiar with its basic functionalities by reading the documentation.
To set up the environment (together with development tools), run:
bash
$ poetry install --with dev
$ poetry run pre-commit install
Then, you will be able to run tests:
bash
$ poetry run pytest
... or check the types:
bash
$ poetry run pyright
Alternatively, you may prefer to work with the right Python environment using:
bash
$ poetry shell
$ pytest
Existing code quality checks
The code quality checks run on GitHub can be seen in .github/workflows/test.yml.
We are using:
- Ruff to lint the code.
- Black to format the code.
- Pyright to check the types.
- Pytest to run the unit tests.
- Interrogate to check the documentation.
Workflow
We use Feature Branch Workflow, in which modifications of the code should happen via small pull requests.
We recommend submitting small pull requests and starting with drafts outlining proposed changes.
Code organisation
- The package code is in
src/pyggdrasil/and is partitioned into subpackages. - The unit tests are in
tests/, and the structure of this directory should reflect the one of the package. - Experimental workflows are in
workflows/, with a description of how to set up the Conda environment inworkflows/README.md
Origin & Authorship
This package originates from Gordon J Köhn's MSc Thesis: Quantifying MCMC Exploration of Tumour Progression Tree Spaces in 2023 at ETH Zürich. Paweł Czyż and Prof. Dr Niko Beerenwinkel supervised this project as part of the Computational Biology Group at the Department of Biosystems Science and Engineering.
Owner
- Name: Computational Biology Group (CBG)
- Login: cbg-ethz
- Kind: organization
- Location: Basel, Switzerland
- Website: https://www.bsse.ethz.ch/cbg
- Twitter: cbg_ethz
- Repositories: 91
- Profile: https://github.com/cbg-ethz
Beerenwinkel Lab at ETH Zurich
GitHub Events
Total
- Watch event: 2
- Delete event: 1
- Issue comment event: 2
- Push event: 4
- Pull request review event: 3
- Pull request review comment event: 1
- Pull request event: 5
- Create event: 2
Last Year
- Watch event: 2
- Delete event: 1
- Issue comment event: 2
- Push event: 4
- Pull request review event: 3
- Pull request review comment event: 1
- Pull request event: 5
- Create event: 2
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Gordon Julian Koehn | g****n@k****t | 94 |
| Paweł Czyż | p****z@a****h | 11 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 85
- Total pull requests: 104
- Average time to close issues: 21 days
- Average time to close pull requests: 4 days
- Total issue authors: 2
- Total pull request authors: 2
- Average comments per issue: 1.28
- Average comments per pull request: 0.79
- Merged pull requests: 92
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- gordonkoehn (72)
- pawel-czyz (13)
Pull Request Authors
- gordonkoehn (95)
- pawel-czyz (11)