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Updated: May 8, 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

Statistical analysis of metabolomics data.

Alysha M De Livera¹, Moshe Olshansky, Terence P Speed

¹Metabolomics Australia, Bio21 Institute (Molecular Science and Biotechnology Institute), The University of Melbourne, Melbourne, Australia.

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

|August 22, 2013

Summary

This summary is machine-generated.

Statistical analysis is crucial for metabolomics experiments. This chapter covers removing unwanted variation and identifying significant metabolite changes for better data interpretation.

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Last Updated: May 8, 2026

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

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Published on: November 29, 2024

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Published on: March 14, 2013

Area of Science:

Metabolomics
Statistical analysis
Bioinformatics

Background:

Metabolomics experiments generate complex datasets requiring robust statistical methods.
Unwanted variation can obscure true biological signals in metabolomics data.
Accurate identification of differentially abundant metabolites is key to biological discovery.

Purpose of the Study:

To outline essential statistical considerations in metabolomics data analysis.
To describe methods for removing unwanted variation.
To explain techniques for identifying differentially abundant metabolites.

Main Methods:

Statistical modeling for noise reduction.
Hypothesis testing for differential abundance analysis.
Exploratory data analysis techniques.

Main Results:

Effective strategies for mitigating technical and biological variability.
Identification of key metabolites that differ between experimental conditions.
Improved confidence in metabolomics findings through rigorous statistical application.

Conclusions:

Sound statistical practices are fundamental for reliable metabolomics research.
Proper data normalization and variance assessment enhance analytical power.
Statistical methods are indispensable tools for advancing metabolomics insights.