Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.

Journal: Metabolomics : Official journal of the Metabolomic Society
Published Date: Jul 1, 2025

Abstract

INTRODUCTION: Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment-even from the same cell lysate.

Authors

  • Chi Yen Tseng
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA. chiyen_tseng@lanl.gov.
  • Jessica A Salguero
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Joshua D Breidenbach
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Emilia Solomon
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Claire K Sanders
  • Tara Harvey
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • M Grace Thornhill
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Salvator J Palmisano
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Zachary J Sasiene
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Brett R Blackwell
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Ethan M McBride
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Kes A Luchini
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Erick S LeBrun
    LMI, Colorado Springs, CO, 80919, USA.
  • Marc Alvarez
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Phillip M Mach
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
  • Emilio S Rivera
    Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA.
  • Trevor G Glaros
    Biochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.

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