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NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data.

Alysha M De Livera1, Gavriel Olshansky2, Julie A Simpson3

  • 1Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3800, Australia. alyshad@unimelb.edu.au.

Metabolomics : Official Journal of the Metabolomic Society
|March 5, 2019
PubMed
Summary
This summary is machine-generated.

Normalization is crucial for metabolomics data analysis. NormalizeMets software aids researchers in comparing normalization methods using either Excel or R, ensuring accurate data interpretation and reproducible results.

Keywords:
ExcelNormalizationRSoftwareStatistical analysis

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Unwanted variation is inherent in metabolomics studies, necessitating robust normalization techniques.
  • Current normalization strategies for metabolomics data are diverse, leading to challenges in method selection.
  • Comparative evaluation of normalization methods is essential for reliable data analysis.

Purpose of the Study:

  • To introduce NormalizeMets, a software tool for the comparative evaluation of metabolomics data normalization methods.
  • To provide both data-oriented and biological researchers with accessible strategies for selecting appropriate normalization techniques.
  • To facilitate the assessment, selection, and implementation of suitable normalization methods.

Main Methods:

  • Development of NormalizeMets, integrating a graphical user interface within Microsoft Excel and a package for the R statistical environment.
  • Provision of downloadable R package and vignette for workflow description.
  • Availability of Excel interface and user guide for ease of use.

Main Results:

  • NormalizeMets enables comparative evaluation of normalization methods based on dataset-specific criteria.
  • The software guides researchers in selecting and implementing appropriate normalization strategies.
  • Facilitates data visualization, clustering, classification, biomarker identification, and correlation analysis.

Conclusions:

  • NormalizeMets offers a dual approach (Excel and R) for normalization method comparison, catering to diverse user preferences.
  • The software supports reproducible research by handling data locally.
  • Provides a comprehensive solution for normalization and downstream statistical analysis in metabolomics.