G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics

Highlights

• Novelty: G-Aligner enables comprehensive analysis of all potential correspondences among features all runs for the first time. G-Aligner treats features and potential correspondences as nodes and edges of a multipartite graph, converts the feature matching problem as a multidimensional assignment problem (MAP), and proposes three combinatorial optimization methods to solve the MAP.
• Accuracy: G-Aligner achieved the best performance in comparison with popular feature alignment methods in MZmine2, OpenMS and XCMS on two public metabolomics benchmark datasets.
• Reliability: G-Aligner achieved the best performance on manually annotated feature lists and untargeted extracted features of MZmine2, OpenMS and XCMS, and helped all compared software obtaining more accurate result by integrating G-Aligner into their workflow.
• Open source: We open-sourced G-Aligner under a permissive license to promote the accuracy of MS data analysis more broadly.
• Dataset: We manually annotated a feature dataset for three public benchmark datasets, which contains m/z, RT, area information of library analytes and can be used in evaluations of feature detection, quantification and alignment accuracy.

Datasets

Raw MS files of the metabolomics datasets can be downloaded at Google Drive.

The mzML files of the metabolomics datasets can be downloaded at Zenodo.

Targeted annotation results, evaluation results and evaluation methods can be downloaded at Zenodo.

Setup

1. Prepare the python environment based on your system and hardware.

2. Install the dependencies. Here we use ROOT_PATH to represent the root path of G-Aligner.

   cd ROOT_PATH\third_party\

   python setup.py install

   cd ROOT_PATH

   pip install -r requirements.txt

Run G-Aligner

Supported formats

Feature extraction rsults in csv format, containing m/z, RT and area columns.

Demos

Our demos can help you reproduce the evaluation results.

Place the data download from the Zenodo repository as follows.

G-Aligner-master
├── data
│   ├── MTBLS562
│   ├── MTBLS562_results_metapro
│   ├── MTBLS562_results_mzmine2
│   ├── MTBLS562_results_openms
│   ├── MTBLS562_results_xcms
│   ├── QE_HF
│   ├── QE_HF_results_metapro
│   ├── QE_HF_results_mzmine2
│   ├── QE_HF_results_openms
│   ├── QE_HF_results_xcms
│   ├── TripleTOF_6600
│   ├── TripleTOF_6600_results_metapro
│   ├── TripleTOF_6600_results_mzmine2
│   ├── TripleTOF_6600_results_openms
│   ├── TripleTOF_6600_results_xcms
│   ├── evaluate_metapro_galigner.py
│   ├── evaluate_metapro_mzmine2.java
│   ├── evaluate_metapro_openms.py
│   ├── evaluate_metapro_xcms.Rmd
│   ├── evaluate_mzmine2_galigner.py
│   ├── evaluate_openms_galigner.py
│   ├── evaluate_xcms_galigner.py
│   ├── metapro_result_comparison.py
│   ├── software_result_comparison.py

• To run the benchmark scripts:

cd ROOT_PATH

python data/metapro_result_comparison.py

python data/software_result_comparison.py

• To analyze with G-Aligner:

cd ROOT_PATH

Change the parameters in data/evaluate_metapro_galigner.py

python data/evaluate_metapro_galigner.py

Feature alignment results are saved in experiment folder.

Citation

Cite our paper at:

@article{wang2023,
  title={G-Aligner: a graph-based feature alignment method for untargeted LC--MS-based metabolomics},
  author={Wang, Ruimin and Lu, Miaoshan and An, Shaowei and Wang, Jinyin and Yu, Changbin},
  journal={BMC bioinformatics},
  volume={24},
  number={1},
  pages={431},
  year={2023},
  publisher={Springer},
  doi={10.1186/s12859-023-05525-4}
}

License

G-Aligner is an open-source tool, using Mulan Permissive Software License，Version 2 (Mulan PSL v2)