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Updated: Apr 17, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Extracting meaningful information from metabonomic data using multivariate statistics.

¹Fios Genomics Ltd, Nine Edinburgh Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK, max.bylesjo@fiosgenomics.com.

Methods in Molecular Biology (Clifton, N.J.)

|February 14, 2015

Summary

This summary is machine-generated.

Metabonomics uses advanced techniques like NMR and mass spectrometry to analyze complex biological data. This chapter details multivariate statistical methods, including PCA, PLS, and OPLS, for extracting meaningful insights from metabonomic datasets.

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Last Updated: Apr 17, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

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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

Area of Science:

Metabolomics and Systems Biology
Analytical Chemistry
Bioinformatics

Background:

Metabonomics involves identifying and quantifying small molecules in biological samples.
High-throughput techniques like NMR and chromatography/mass spectrometry generate large, complex datasets.
Specialized data analysis approaches are essential for interpreting metabonomic data.

Purpose of the Study:

To describe multivariate statistical and analysis tools for metabonomic data.
To focus on the application and interpretation of latent variable methods.
To provide demonstrations using example data for key multivariate analysis steps.

Main Methods:

Multivariate statistical analysis
Latent variable methods including Principal Component Analysis (PCA)
Partial Least Squares (PLS) and Orthogonal PLS (OPLS)

Main Results:

Detailed descriptions of key steps in multivariate data analysis.
Demonstrations of PCA, PLS, and OPLS using example datasets.
Guidance on interpreting results from latent variable methods.

Conclusions:

Multivariate statistics are crucial for extracting information from high-dimensional metabonomic data.
Latent variable methods like PCA, PLS, and OPLS provide powerful tools for analysis.
Understanding these methods enhances the interpretation of metabolic profiles.