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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Statistical analysis in metabolic phenotyping.

Benjamin J Blaise1,2,3, Gonçalo D S Correia1,4, Gordon A Haggart1,4

  • 1Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, UK.

Nature Protocols
|July 29, 2021
PubMed
Summary
This summary is machine-generated.

This study presents an efficient protocol and software for analyzing complex metabolic phenotyping data from mass spectrometry and NMR spectroscopy. It provides a streamlined workflow for statistical analysis, making advanced methods accessible without prior coding knowledge.

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Area of Science:

  • Biomedical Research
  • Metabolomics
  • Statistical Analysis

Background:

  • Metabolic phenotyping is crucial for translational biomedical research.
  • Advanced analytical techniques like mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy generate complex data.
  • Existing protocols for data acquisition are available, but efficient statistical analysis methods are needed.

Purpose of the Study:

  • To propose an efficient protocol and software for the statistical analysis of metabolic data.
  • To provide accessible solutions for the complete data analytics workflow in metabolic phenotyping.
  • To ensure robust experimental designs and reliable results through statistical power calculations.

Main Methods:

  • Development of a comprehensive protocol and accompanying software for metabolic data analysis.
  • Inclusion of solutions for scaling, normalization, outlier detection, and multivariate analysis.
  • Provision of methods for biomarker selection, validation, multiple testing correction, and model performance evaluation.

Main Results:

  • An efficient, user-friendly protocol for statistical analysis of metabolic phenotyping data.
  • Software implementation requiring no prior coding skills.
  • Demonstration of the protocol's applicability with a two-group classification study and epidemiological cohort data.

Conclusions:

  • The proposed protocol offers a rigorous and accessible approach to analyzing metabolic phenotyping data.
  • It facilitates robust experimental design and reliable data interpretation.
  • The protocol is adaptable to various experimental designs and modeling strategies.