Modified code from Arseha

This repository mainly contains the origial code with an added convenient high-level Python API for easier use. For example for interactive use in the JupyterLab. The original code contains code for a NeuralNetwork based approach for peak detection in untargeted metabolomics. This high-level API makes it much easier to use the code in your own Python script or notebook. In the following a small example on how to use it and how to install it.

Example usage

from ms_peakonly import PeakOnly
from glob import glob

# Get a list of file names to process (I believe onl mzML files are supported)
fns = glob('/my/metabolomics/directory/*.mzML')

# Instantiate the engine if the neural network weights
# are not already downloaded this will
# also download the models. 
po = PeakOnly(model_dir='/my/model/directory/')

# Simply pass the list of filenames to the `process` method.
table = po.process(fns)

Installation

This API can be installed with pip or python setup.py install and is then available via a simple import in your Python environment as shown above.

pip install git+https://github.com/sorenwacker/ms-peakonly