A toolkit for multi-omic phenotypic space exploration.
Phenonaut is a framework for applying workflows to multi-omics data. Originally targeting high-content imaging and the exploration of phenotypic space, with different visualisations and metrics, Phenonaut allows now operates in a data agnostic manner, allowing users to describe their data (potentially multi-view/multi-omics) and apply a series of generic or specialised data-centric transforms and measures.
Phenonaut operates in 2 modes:
- As a Python package, importable and callable within custom scripts.
- Operating on a workflow defined in either YAML, or JSON, allowing integration of complex chains of Phenonaut instructions to be integrated into existing workflows and pipelines. When built as a package and installed, workflows can be executed with:
python -m phenonaut workflow.yml.
After installing phenonaut into a kernel, dont forget to register it with Jupyter:
python -m ipykernel install --user --name=<ENVIRONMENT NAME>
Datasets are read into the dataset class, aided by a yaml file describing the underlying data (see config/ for example yaml data definition files). Pandas dataframes are created representing the data (a Phenonaut object may hold multiple dataset objects), along with two additional pieces of data.
- A features list, accessible with .features property of a dataframe. Initially defined by the data definition workflow.
- perturbation_column, optional column which gives a unique ID to the treatment performed on the well/vial/data.
- Metadata, optional dictionary containing metadata for the dataset.
Phenonaut can be easily pip installed:
pip install phenonautRemember to add the new environment to jupyter ipykernels (replacing phenonaut_env with the name of your python environment):
python -m ipykernel install --user --name=phenonaut_envCopyright © The University of Edinburgh, 2024.
Development has been supported by GSK.