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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Metabolomic Bioinformatic Analysis.

Allyson L Dailey1

  • 1Department of Chemistry and Biochemistry, George Mason University, 10920 George Mason Circle, MS1A9, Manassas, VA, 20110, USA. adailey3@gmu.edu.

Methods in Molecular Biology (Clifton, N.J.)
|May 15, 2017
PubMed
Summary
This summary is machine-generated.

This study presents R protocols for analyzing large metabolomic datasets. It details common multivariate statistical techniques for understanding complex biological small molecule data.

Keywords:
Correlation networkData analysisDendrogramsMetabolomicsPCAPheatmapQgraphR programming languageRgl

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Metabolomics investigates small molecules in biological systems.
  • Metabolomic datasets often contain thousands of variables.
  • Handling large datasets requires advanced analytical methods.

Purpose of the Study:

  • To provide protocols for R-based analysis of metabolomic data.
  • To demonstrate common multivariate statistical techniques for metabolomics.
  • To facilitate the understanding of complex metabolomic datasets.

Main Methods:

  • Utilizing the R programming language.
  • Implementing multivariate statistical techniques.
  • Analyzing large-scale metabolomic data matrices.

Main Results:

  • Protocols for applying common multivariate statistical methods in R.
  • Demonstration of R's utility in handling high-dimensional metabolomic data.
  • Framework for analyzing and interpreting complex small molecule data.

Conclusions:

  • R is a powerful tool for multivariate statistical analysis in metabolomics.
  • The presented protocols aid researchers in analyzing large metabolomic datasets.
  • This work enhances the understanding of biological systems through data analysis.