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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis.

Lingli Deng^1,2, Lei Ma², Kian-Kai Cheng³

¹Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System, East China University of Technology, Nanchang 330013, China.

Journal of Proteome Research

|May 18, 2021

Summary

This summary is machine-generated.

Overlapping metabolite sets in metabolomics can cause false positives in pathway analysis. We developed overlapping group PLS (ogPLS) to accurately identify perturbed metabolic pathways by properly weighting shared metabolites.

Keywords:

debiasing regularization group lasso metabolite set enrichment analysis (MSEA)overlapping group partial least squares (ogPLS)stable selection

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Area of Science:

Metabolomics
Bioinformatics
Systems Biology

Background:

Metabolite set enrichment analysis (MSEA) identifies perturbed metabolic pathways.
Standard MSEA assumes mutually exclusive pathways, but real pathways overlap.
Overlapping metabolite sets lead to false positives in MSEA.

Purpose of the Study:

To propose a novel method for overlapping metabolite set enrichment analysis.
To improve the accuracy and reduce false positives in pathway identification.
To offer a robust alternative for metabolomics data interpretation.

Main Methods:

Developed overlapping group PLS (ogPLS) model with a sparse scheme.
Decomposed weight vectors and applied group lasso penalty for metabolite weighting.
Employed debiasing regularization and stable selection for pathway identification.

Main Results:

ogPLS showed a higher true discovery rate than Global-test and MB-PLS-PIP on simulated data.
ogPLS was less prone to select pathways with highly overlapped detected metabolite sets in real data.
The proposed method demonstrated higher accuracy and lower false-positive rates.

Conclusions:

ogPLS effectively addresses the challenge of overlapping metabolite sets in MSEA.
The method provides a more robust and accurate approach for pathway analysis in metabolomics.
ogPLS facilitates improved biological interpretation of complex metabolomics datasets.