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MAMSI is a Python framework designed for the integration of multi-assay mass spectrometry datasets. In addition, the MAMSI framework provides a platform for linking statistically significant features of untargeted multi-assay liquid chromatography – mass spectrometry (LC-MS) metabolomics datasets into clusters defined by their structural properties based on mass-to-charge ratio (m/z) and retention time (RT).

N.B. the framework was tested on metabolomics phenotyping data, but it should be usable with other types of LC-MS data.

Overview

Features

  • Data integration analysis using the Multi-Block Partial Least Squares (MB-PLS) [1] algorithm. The MamsiPls class inherits from the mbpls package [1]. For more information on MB-PLS, please visit mbpls Documentation.
  • Multi-Block Variable Importance in Projection (MB-VIP) [2].
  • Estimation of statistically significant features (variables) using MB-VIP and permutation testing.
  • Linking significant features into clusters defined by structural properties of metabolites.
  • Feature network links.
  • Annotation of untargeted LC-MS features (only supported for assays analysed by the National Phenome Centre).

Sources and Materials

The package source code is accessible via GitHub at https://github.com/kopeckylukas/py-mamsi

Training materials including sample data can be found at https://github.com/kopeckylukas/py-mamsi-tutorials.

Installation

Dependencies

  • mbpls==1.0.4
  • pandas
  • numpy
  • matplotlib
  • scipy
  • scikit-learn
  • seaborn
  • networkx
  • pyvis

User Installation

Installing with Pip

You can install MAMSI from PyPI using pip: 

pip install mamsi

Installing from source-code

You can install MAMSI from source code given you have installed both Python (>=3.9) and PIP.

First, clone the repository from GitHub to your computer. You can use following commands if you have a version of Git installed on your computer:

git clone https://github.com/kopeckylukas/py-mamsi

cd py-mamsi

When you are in the cloned project folder, type the following code to install MAMSI and all dependencies: 

pip install .

Alternatively, you can install dependencies using pip and MAMSI using Python: 

pip install -r requirements.txt
python setup.py develop

Issues and Collaboration

Thank you for supporting the MAMSI project. MAMSI is an open-source software and welcomes any form of contribution and support.

Issues

Please submit any bugs or issues via the project's GitHub issue page and any include details about the (mamsi.__version__) together with any relevant input data/metadata.

Collaboration

Pull requests

You can actively collaborate on MAMSI package by submitting any changes via a pull request. All pull requests will be reviewed by the MAMSI team and merged in due course.

Contributions

If you would like to become a contributor on the MAMSI project, please contact Lukas Kopecky.

Acknowledgement

This package was developed as part of Lukas Kopecky's PhD project at Imperial College London, funded by Waters UK. It is free to use, published under BSD 3-Clause licence.

The authors of this package would like to acknowledge the authors of the mbpls package [1] which became the backbone of MAMSI. For more information on MB-PLS, please visit MB-PLS Documentation.

Further, we would like to thank Prof Simon Lovestone and Dr Shivani Misra for allowing us to use their data, AddNeuroMed [3] and MY Diabetes [5] respectively, for the development of this package.

Citing us

If you use MAMSI in a scientific publication, we would appreciate citations.

Release

@misc{MAMSI2024,
  author       = {Lukas Kopecky, Elizabeth J Want, Timothy MD Ebbels},
  title        = {MAMSI: Multi-Assay Mass Spectrometry Integration},
  year         = 2024,
  url          = {https://doi.org/10.5281/zenodo.13619607},
  note         = {Zenodo. Version 1.0.0},
  doi          = {10.5281/zenodo.13619607}
}

Publication

The MAMSI publication is currently under the review process.

References

[1] A. Baum and L. Vermue, "Multiblock PLS: Block dependent prediction modeling for Python," J. Open Source Softw., vol. 4, no. 34, 2019, doi: 10.21105/joss.01190.

[2] C. Wieder et al., "PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration," PLOS Comput. Biol., vol. 20, no. 3, p. e1011814, Mar 2024, doi: 10.1371/journal.pcbi.1011814.

[3] S. Lovestone et al., "AddNeuroMed—The European Collaboration for the Discovery of Novel Biomarkers for Alzheimer's Disease," Ann. N. Y. Acad. Sci, vol. 1180, no. 1, pp. 36-46, 2009, doi: 10.1111/j.1749-6632.2009.05064.x.

[4] A. M. Wolfer et al., "peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC–MS profiling datasets," Bioinformatics, vol. 37, no. 24, pp. 4886-4888, 2021, doi: 10.1093/bioinformatics/btab433.

[5] S. Misra et al., "Systematic screening for monogenic diabetes in people of South Asian and African Caribbean ethnicity: Preliminary results from the My Diabetes study," presented at the Diabet. Med., Mar 2018.

[6] M. Lewis et al., “An Open Platform for Large Scale LC-MS-Based Metabolomics ,” ChemRxiv, 2022. doi: 10.26434/chemrxiv-2022-nq9k0.

[7] C. A. Smith et al., "XCMS:  Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification," Anal. Chem., vol. 78, no. 3, pp. 779-787, Feb 2006, doi: 10.1021/ac051437y.

[8] C. J. Sands et al., "The nPYc-Toolbox, a Python module for the pre-processing, quality-control and analysis of metabolic profiling datasets," Bioinformatics, vol. 35, no. 24, pp. 5359-5360, 2019, doi: 10.1093/bioinformatics/btz566.

Version History

v1.0.2

New Features

  • New method 'MamsiPls.block_importance()': Calculate the block importance for each block in the multiblock PLS model and plot the results.

Minor Bug Fixes and Behaviour Changes

  • Behavioural changes for MamsiPls.mb_vip(): The MB-VIP plot is now rendered by default, scores are not returned by default. New default arguments (plot=True, get_scores=False).
  • Argument changes for MamsiPls.estimate_lv(): Old Arguments (no_folds, n_components) changed to (n_slplits, max_components) respectively.
  • Plots: 'Verdana' is no longer the default font. The default font changed to Matplotlib default 'DejaVu Sans'.
  • Updates to MamsiStructSearch class to comply with future warnings - Pandas 3.0.

v1.0.1

Minor Bugs Update

  • Fixes instances where flattened correlation clusters were misaligned to structural clusters.
  • Readme licence badge links directly to GitHub licence file (URL).

v1.0.0

Initial Release