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Related Experiment Video

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Bayesian multiple hypotheses testing in compositional analysis of untargeted metabolomic data.

Julie de Sousa1, Ondřej Vencálek2, Karel Hron2

  • 1Department of Mathematical Analysis and Applications of Mathematics, Palacký University, 17. Listopadu 1192/12, 771 46, Olomouc, Czech Republic; Department of Clinical Biochemistry, University Hospital Olomouc, I.P. Pavlova 185/6, 779 00, Olomouc, Czech Republic; Laboratory of Metabolomics, Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University, Hněvotínská 5, 779 00, Olomouc, Czech Republic.

Analytica Chimica Acta
|January 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian statistical method for clinical metabolomics, improving biomarker discovery by analyzing the compositional nature of metabolic data. The new approach enhances the identification of disease-related metabolic differences.

Keywords:
Bayesian inferenceCompositional dataHigh-dimensional dataMultiple hypotheses testingUntargeted metabolomicsVolcano plot

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Area of Science:

  • Metabolomics
  • Biostatistics
  • Biomarker Discovery

Background:

  • Clinical metabolomics seeks to identify metabolic differences between patient and control groups for disease understanding and biomarker identification.
  • Traditional univariate statistical methods (e.g., t-tests) and volcano plots often yield misleading results due to multiple testing and ignoring data composition.
  • Metabolomic data is compositional, meaning the relative abundance of metabolites (ratios) is crucial for accurate analysis.

Purpose of the Study:

  • To propose a Bayesian statistical approach for univariate analysis in clinical metabolomics.
  • To account for the compositional nature of metabolomic data using logratio coordinates.
  • To introduce a Bayesian volcano plot with b-values for improved visualization of biomarker significance.

Main Methods:

  • Developed a Bayesian counterpart to traditional univariate statistical tests for metabolomic data.
  • Incorporated logratio coordinates to handle the compositional characteristics of metabolomic profiles.
  • Introduced b-values and a Bayesian volcano plot to visualize posterior highest density intervals.

Main Results:

  • The proposed Bayesian method was illustrated using datasets from patients with 3-hydroxy-3-methylglutaryl-CoA lyase deficiency and medium-chain acyl-CoA dehydrogenase deficiency.
  • Simulations demonstrated the method's stability against sample loss and systematic measurement errors.
  • The compositional approach proved beneficial for robust biomarker identification.

Conclusions:

  • The Bayesian approach offers a more robust statistical framework for clinical metabolomics compared to traditional methods.
  • Accounting for the compositional nature of metabolomic data enhances the reliability of biomarker discovery.
  • The developed Bayesian volcano plot provides a more informative visualization for identifying significant metabolic biomarkers.