MetaboScope: a statistical toolbox for analyzing <SUP>1</SUP>H nuclear magnetic resonance spectra from human clinical studies
Author(s)
Type
Journal Article
Abstract
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 preprocessing pipelines, multivariate modeling tools including Principal Components Analysis (PCA), Orthogonal-Projection to Latent Structure Discriminant Analysis (OPLS-DA), 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.
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 preprocessing pipelines, multivariate modeling tools including Principal Components Analysis (PCA), Orthogonal-Projection to Latent Structure Discriminant Analysis (OPLS-DA), 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.
Editor(s)
Yu, Guoqiang
Date Issued
2024-11-19
Date Acceptance
2024-09-24
Citation
Bioinformatics Advances, 2024, 4 (1)
ISSN
2635-0041
Publisher
Oxford University Press
Journal / Book Title
Bioinformatics Advances
Volume
4
Issue
1
Copyright Statement
© The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39569319
PII: vbae142
Subjects
Life Sciences & Biomedicine
Mathematical & Computational Biology
MULTIBLOCK
Science & Technology
SPECTROSCOPY
Publication Status
Published
Coverage Spatial
England
Article Number
ARTN vbae142
Date Publish Online
2024-10-28