SPA-STOCSY: an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra

Abstract Motivation Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistica...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2023-10, Vol.39 (10)
Main Authors: Han, Xu, Wang, Wanli, Ma, Li-Hua, AI-Ramahi, Ismael, Botas, Juan, MacKenzie, Kevin, Allen, Genevera I, Young, Damian W, Liu, Zhandong, Maletic-Savatic, Mirjana
Format: Article
Language:English
Subjects:
Animals
Cluster Analysis
Drosophila melanogaster
Humans
Magnetic Resonance Imaging
Magnetic Resonance Spectroscopy - methods
Metabolomics - methods
Original Paper
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Summary:Abstract Motivation Nuclear magnetic resonance spectroscopy (NMR) is widely used to analyze metabolites in biological samples, but the analysis requires specific expertise, it is time-consuming, and can be inaccurate. Here, we present a powerful automate tool, SPatial clustering Algorithm-Statistical TOtal Correlation SpectroscopY (SPA-STOCSY), which overcomes challenges faced when analyzing NMR data and identifies metabolites in a sample with high accuracy. Results As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset. It first investigates the covariance pattern among datapoints and then calculates the optimal threshold with which to cluster datapoints belonging to the same structural unit, i.e. the metabolite. Generated clusters are then automatically linked to a metabolite library to identify candidates. To assess SPA-STOCSY’s efficiency and accuracy, we applied it to synthesized spectra and spectra acquired on Drosophila melanogaster tissue and human embryonic stem cells. In the synthesized spectra, SPA outperformed Statistical Recoupling of Variables (SRV), an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the biological data, SPA-STOCSY performed comparably to the operator-based Chenomx analysis while avoiding operator bias, and it required
ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btad593