Tool-chain for relating metabolite MS data with bio-activity data.
For complete usage of the CLI see our documentation.
Using Anaconda Python, create a virtual environment with all the necessary dependencies, and then install the toolchain into that environment:
conda env create -f environment.yml
conda activate npanalyst
pip install .
You can use Docker to run the npanalyst CLI by binding a local volume.
To get the Docker image, you must build it.
docker build -t npanalyst_cli .Then running the CLI works as
See the CLI help command
docker run -it npanalyst_cli As an example of bind mounting a local volume.
docker run -it -v PATH/TO/DATA:/data npanalyst_cli import -i /data/GNPS.graphml -o /data -t GNPS -vUsing an rtree to build connected component graphs of mz features with overlapping error ranges (in all dimensions). These are then combined (averaged) and represent a replicated or basketed feature.
Right now things are implemented via a CLI (npanalyst) which will process a folder of csv files or basket a folder
of replicated CSV's (output from replicate task).
npanalyst --help should show all the features and usage.
A lot of the parameters are specified via a config file that defaults to npanalyst.configuration.DEFAULT_CONFIG
this will change a little as things get tuned. You can generate a config file using npanalyst get_config which will
produce a ./config.json file.
Pytest has been used to implement basic unit and full pipeline (called integration here) tests.
To run the tests, make sure you have pytest installed (Eg. pip install pytest), and simply run the pytest command.
You can run the unit and integration tests separately by specifying a path to test pytest tests/unit or pytest tests/integration.