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Related Experiment Videos

Normalization method for metabolomics data using optimal selection of multiple internal standards.

Marko Sysi-Aho1, Mikko Katajamaa, Laxman Yetukuri

  • 1VTT Technical Research Centre of Finland, Tietotie 2, FIN-02044 VTT, Espoo, Finland. marko.sysi-aho@vtt.fi <marko.sysi-aho@vtt.fi>

BMC Bioinformatics
|March 17, 2007
PubMed
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A new method called NOMIS normalizes metabolomics data by using internal standards to reduce systematic errors. This approach improves data quality for lipidomic profiles and aids in selecting optimal standards for analysis.

Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biochemistry

Background:

  • Metabolomics relies on detecting biological variability, necessitating robust data preprocessing.
  • Removing systematic errors is crucial for accurate metabolomics, but chemical diversity complicates normalization.
  • Effective normalization is challenging due to varied responses of metabolites to experimental conditions.

Purpose of the Study:

  • To develop a novel normalization method (NOMIS) for metabolomics data.
  • To improve the removal of systematic variations in complex biological samples.
  • To provide a flexible approach applicable to different experimental designs.

Main Methods:

  • Utilized variability information from multiple internal standard compounds.

Related Experiment Videos

  • Developed the Normalization by Intrinsic Standard (NOMIS) method.
  • Applied and validated the method on mouse liver lipidomic profiles using UPLC-HRMS.

    • Main Results:

    • The NOMIS method effectively reduced systematic error across metabolite peaks.
    • NOMIS outperformed common normalization techniques (l2 norm, retention time region specific standards).
    • Demonstrated the ability to select optimal internal standard combinations for normalization.

    Conclusions:

    • NOMIS is applicable as a one-step or two-step normalization method.
    • The method can be used in analytical development to select optimal internal standards.
    • NOMIS enhances the reliability of metabolomics data across various matrices and platforms.