GgmLipidClassifier (GLC) is an open-source Python package designed to systematically predict lipid class directly from MS1-only data in untargeted liquid chromatography–mass spectrometry (LC–MS) lipidomic workflows. GLC integrates accurate-mass database searching with Gaussian graphical models (GGM) estimated from feature intensities to predict lipid subclass and main class according to the LIPID MAPS Structural Database (LMSD) ontology. Requiring just a feature table, GLC predicts lipid class for most detected features and does not require prior annotation or MS2 for prediction.
Documentation is avaliable on our Read the Docs page.
The package source code is accessible via GitHub at: https://github.com/trix98/GLC
You can install GLC from PyPI using pip:
pip install ggmlipidclassifierThank you for supporting the GLC project. GLC is an open-source software and welcomes any form of contribution and support.
Please submit any bugs or issues via the project's GitHub page issue page include any details about the (glc.version) together with any relevant input data/metadata.
You can actively colloborate on the GLC package by submitting any changes via a pull request. All pull requests will be reviewed by the GLC team and merged in due course.
If you would like to become a contibuter on the GLC project, please contact Thomas Rix at tir21@ic.ac.uk
This package was developed as part of Thomas Rix's PhD project at Imperial College London, supported by the European Union project HUMAN (grant EC101073062, UKRI EP/X035840/1). It is free to use, published under BSD 3-Clause licence.
The authors gratefully acknowledge Prof Simon Lovestone for permission to use the AddNeuroMed dataset.
Dr María Gómez-Romero for help with dataset pre-processing.
René Neuhaus and Sara Martínez for their support on the data treatment of the metformin-HIIE dataset.
If you found this package useful, please consider citing us.
The article is currently in the process of journal submission.