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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Statistical methods and resources for biomarker discovery using metabolomics.

Najeha R Anwardeen1, Ilhame Diboun2, Younes Mokrab2

  • 1Research and Graduate Studies, Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar.

BMC Bioinformatics
|June 15, 2023
PubMed
Summary

Metabolomics studies biochemical changes for disease insights. This review surveys statistical tools for analyzing metabolomics data to discover biomarkers for clinical use.

Keywords:
Analytical workflowMetabolomicsMetabolomics toolsMultivariateStatistical methodsUnivariate

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Area of Science:

  • Biochemistry
  • Genomics
  • Clinical Diagnostics

Background:

  • Metabolomics offers deep insights into physiological states and disease mechanisms.
  • Metabolic profiles are sensitive to genetic and environmental factors.
  • Abundant high-throughput metabolomics data necessitates robust statistical analysis.

Purpose of the Study:

  • To review statistical approaches for biomarker discovery in metabolomics.
  • To identify available statistical tools for metabolomics data analysis and interpretation.

Main Methods:

  • Survey of statistical methodologies in metabolomics.
  • Compilation of relevant statistical software and tools.
  • Focus on biomarker discovery applications.

Main Results:

  • Identification of various statistical techniques applicable to metabolomics.
  • Overview of tools supporting data analysis and interpretation.
  • Highlighting the importance of statistical rigor for clinical translation.

Conclusions:

  • Statistical analysis is crucial for extracting meaningful insights from metabolomics data.
  • Appropriate tools are essential for reliable biomarker discovery.
  • Advancements in metabolomics require sophisticated statistical approaches for clinical applications.