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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics.

Izabella Surowiec1, Erik Johansson2, Frida Torell1

  • 1Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, 901 81 Umeå, Sweden.

Metabolomics : Official Journal of the Metabolomic Society
|September 12, 2017
PubMed
Summary

This study introduces a multivariate method for selecting representative samples and combining metabolomics data from multiple batches, crucial for large cohort studies and accurate compound quantification.

Keywords:
MetabolomicsMulti-batch analysisOPLSRepresentative sample selection

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Area of Science:

  • Metabolomics
  • Systems Biology
  • Biostatistics

Background:

  • High-throughput 'omics' technologies generate large datasets.
  • Representative sample selection is critical for large cohorts and multi-batch analysis.
  • Metabolomics studies require careful sample selection and data integration.

Purpose of the Study:

  • To present a multivariate strategy for representative sample selection.
  • To integrate results from multi-batch metabolomics experiments.
  • To enable accurate analysis of large sample cohorts.

Main Methods:

  • Multivariate characterization for design of experiment (DOE) based sample selection.
  • Subdivision of samples into four analytical batches for metabolomics profiling.
  • Gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS) analysis.
  • Orthogonal partial least squares discriminant analysis (OPLS-DA) for batch-specific analysis.
  • Averaging OPLS-DA p(corr) vectors for a combined metabolic profile.
  • Jackknifed standard errors for confidence interval calculation.

Main Results:

  • A combined, representative metabolic profile was obtained.
  • Differences between systemic lupus erythematosus (SLE) patients and controls were identified.
  • Potential disturbed metabolic pathways in SLE were elucidated.

Conclusions:

  • DOE-based sample selection ensures diversity and minimizes bias.
  • Combined metabolic profiling allows for unified analysis and interpretation.
  • This strategy is effective for large-scale metabolomics studies.