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MSclassifR: an R package for supervised classification of mass spectra with machine learning methods

Alexandre Godmer, Yahia Benzerara, Nicolas Veziris, Mariette Matondo, Alexandra Aubry, Quentin Giai Gianetto
doi: https://doi.org/10.1101/2022.03.14.484252
Alexandre Godmer
1AP-HP, Sorbonne université, Hôpital Saint-Antoine, Département de Bactériologie, Paris, France
2Sorbonne Université, INSERM, U1135, Centre d’Immunologie et des Maladies Infectieuses, Cimi-Paris, Paris, France
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  • For correspondence: alexandre.godmer@aphp.fr
Yahia Benzerara
1AP-HP, Sorbonne université, Hôpital Saint-Antoine, Département de Bactériologie, Paris, France
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Nicolas Veziris
1AP-HP, Sorbonne université, Hôpital Saint-Antoine, Département de Bactériologie, Paris, France
2Sorbonne Université, INSERM, U1135, Centre d’Immunologie et des Maladies Infectieuses, Cimi-Paris, Paris, France
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Mariette Matondo
3Institut Pasteur, Université de Paris, Proteomics Platform, Mass Spectrometry for Biology Unit, UAR CNRS 2024
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Alexandra Aubry
2Sorbonne Université, INSERM, U1135, Centre d’Immunologie et des Maladies Infectieuses, Cimi-Paris, Paris, France
4AP-HP, AP-HP. Sorbonne-Université, Hôpital Pitié-Salpêtrière, Centre National de Référence des Mycobactéries et de la Résistance des Mycobactéries aux Antituberculeux, Paris, France
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Quentin Giai Gianetto
3Institut Pasteur, Université de Paris, Proteomics Platform, Mass Spectrometry for Biology Unit, UAR CNRS 2024
5Institut Pasteur, Université de Paris, Bioinformatics and Biostatistics HUB, Computational Biology Department
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Abstract

Motivation Classification of mass spectra is essential for identifying microorganisms from matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry. However, spectrally close organisms remain difficult to identify. In this context, we developed the MSclassifR R package to improve the classification of mass spectra. Its open code strengthens the reproducibility of analyzes in the community.

Results We applied the functions of our package to raw mass spectra from three different laboratories. The best workflow available in MSclassifR package achieves near 100% accuracy in all three datasets. Thus, MSclassifR constitutes an interesting alternative for reliable MALDI-TOF based diagnosis.

Availability MSclassifR is freely available online from CRAN repository https://cran.r-project.org/web/packages/MSclassifR/index.html. Two vignettes illustrating how to use the functions of this package from real data sets are also available online to help users.

Contact alexandre.godmer{at}aphp.fr

Supplementary information Supplementary material is available at Bio-informatics online.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 16, 2022.
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MSclassifR: an R package for supervised classification of mass spectra with machine learning methods
Alexandre Godmer, Yahia Benzerara, Nicolas Veziris, Mariette Matondo, Alexandra Aubry, Quentin Giai Gianetto
bioRxiv 2022.03.14.484252; doi: https://doi.org/10.1101/2022.03.14.484252
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MSclassifR: an R package for supervised classification of mass spectra with machine learning methods
Alexandre Godmer, Yahia Benzerara, Nicolas Veziris, Mariette Matondo, Alexandra Aubry, Quentin Giai Gianetto
bioRxiv 2022.03.14.484252; doi: https://doi.org/10.1101/2022.03.14.484252

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