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Updated: Jul 4, 2025

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Pretreating and normalizing metabolomics data for statistical analysis.

Jun Sun1, Yinglin Xia2

  • 1Division of Gastroenterology and Hepatology, Department of Medicine, Department of Microbiology/Immunology, UIC Cancer Center, University of Illinois Chicago, Jesse Brown VA Medical Center Chicago (537), Chicago, IL 60612, USA.

Genes & Diseases
|February 1, 2024
PubMed
Summary
This summary is machine-generated.

Metabolomics, the study of small molecules, requires data preprocessing for analysis. This review covers methods for mass spectrometry and NMR data, crucial for microbiome and metabolome research in precision medicine.

Keywords:
Data centering and scalingData normalizationData transformationMS-Based data preprocessingMissing valuesNMR Data preprocessingOutliersPreprocessing/pretreatment

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Systems Biology

Background:

  • Metabolomics studies small molecules in biological samples, offering insights for precision medicine.
  • Integrating metabolomics with microbiome data reveals microbial roles in health and disease.
  • Metabolomics data complexity necessitates robust preprocessing and normalization before analysis.

Purpose of the Study:

  • To comprehensively review preprocessing and data treatment methods for metabolomics data.
  • To discuss techniques for mass spectrometry (MS) and nuclear magnetic resonance (NMR) based metabolomics.
  • To highlight considerations for handling missing values, outliers, normalization, scaling, and transformation.

Main Methods:

  • Review of established and emerging methods for metabolomics data preprocessing.
  • Comparative analysis of techniques for MS and NMR data.
  • Discussion on strategies for data imputation, outlier detection, normalization, and transformation.

Main Results:

  • Detailed overview of various preprocessing techniques, including their advantages and limitations.
  • Guidance on selecting appropriate methods based on biological context, data characteristics, and statistical analysis.
  • Exploration of preprocessing impacts on microbiome and metabolome integration studies.

Conclusions:

  • Appropriate preprocessing is essential for accurate metabolomics data analysis.
  • Method selection depends on specific research questions and data properties.
  • Effective preprocessing enhances the utility of metabolomics in precision medicine and microbiome research.