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Project Status: proof-of-concept – repository is only intended to be a limited example, demo, or proof-of-concept. build Ruff Code style: black

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

import pyggdrasil as yg

Contributing

See Contributing Guidelines.

Setting up the repository

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: 

$ poetry install --with dev
$ poetry run pre-commit install

Then, you will be able to run tests: 

$ poetry run pytest
... or check the types: 
$ poetry run pyright

Alternatively, you may prefer to work with the right Python environment using: 

$ 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:

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 in workflows/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.