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MetaboScope: A statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies
Journal article   Open access   Peer reviewed

MetaboScope: A statistical toolbox for analyzing 1H nuclear magnetic resonance spectra from human clinical studies

Ruey Leng Loo, Javier Osorio Mosquera, Michael Zasso, Jeremy K Nicholson, Luc Patiny, Elaine Holmes and Julien Wist
Bioinformatics advances, Vol.4(1), vbae142
2024
DOI: https://doi.org/10.1093/bioadv/vbae142
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CC BY V4.0,  Open Access

Abstract

Systems biology Chemometrics Data analysis Pattern recognition
Motivation Metabolic phenotyping, using high-resolution spectroscopic molecular fingerprints of biological samples, has demonstrated diagnostic, prognostic, and mechanistic value in clinical studies. However, clinical translation is hindered by the lack of viable workflows and challenges in converting spectral data into usable information. Results MetaboScope is an analytical and statistical workflow for learning, designing and analyzing clinically relevant 1H nuclear magnetic resonance data. It features modular pre-processing pipelines, multivariate modeling tools including principal components analysis (PCA), orthogonal-projection to latent structure discriminant analysis, and biomarker discovery tools (multiblock PCA and statistical spectroscopy). A simulation tool is also provided, allowing users to create synthetic spectra for hypothesis testing and power calculations. Availability and implementation MetaboScope is built as a pipeline where each module accepts the output generated by the previous one. This provides flexibility and simplicity of use, while being straightforward to maintain. The system and its libraries were developed in JavaScript and run as a web app; therefore, all the operations are performed on the local computer, circumventing the need to upload data. The MetaboScope tool is available at https://www.cheminfo.org/flavor/metabolomics/index.html. The code is open-source and can be deployed locally if necessary. Module notes, video tutorials, and clinical spectral datasets are provided for modeling.

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