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DOE OSTI · 3375694

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

Tseng, Chi Yen [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000279786856)·Salguero, Jessica Annette [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000306684688)·Breidenbach, Joshua David [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000258921879)·Solomon, Emilia A. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000302659792)·Sanders, Claire Kathleen [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000178482642)·Harvey, Tara [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000615322522)·Thornhill, Madison Grace [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)]·Palmisano, Salvator Joseph [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000151204239)·Sasiene, Zachary Joseph [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000160203828)·Blackwell, Brett Reginald [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000312964539)·McBride, Ethan Matthew [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000252472654)·Luchini, Kes Amber [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000297546565)·LeBrun, Erick S. [LMI, Colorado Springs, CO (United States)]·Alvarez, Marc Anthony [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000246791106)·Mach, Phillip Michael [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000308423844)·Rivera, Emilio Sanchez [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000332151092)·Glaros, Trevor Griffiths [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000199226330)
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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. Objectives To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets. Methods We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance. Results Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others. Conclusion Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.

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60 APPLIED LIFE SCIENCESLipidomicsMetabolomicsMulti-omicsOmicsProteomicsTime-courseevaluationnormalization

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Tseng, Chi Yen [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000279786856), Salguero, Jessica Annette [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000306684688), Breidenbach, Joshua David [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000258921879), Solomon, Emilia A. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000302659792), Sanders, Claire Kathleen [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000178482642), Harvey, Tara [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000615322522), Thornhill, Madison Grace [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Palmisano, Salvator Joseph [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000151204239), Sasiene, Zachary Joseph [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000160203828), Blackwell, Brett Reginald [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000312964539), McBride, Ethan Matthew [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000252472654), Luchini, Kes Amber [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000297546565), LeBrun, Erick S. [LMI, Colorado Springs, CO (United States)], Alvarez, Marc Anthony [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000246791106), Mach, Phillip Michael [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000308423844), Rivera, Emilio Sanchez [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000332151092), Glaros, Trevor Griffiths [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000199226330). 2025-07-01. Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets. https://doi.org/10.1007/s11306-025-02297-1

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