Evidence map›Paper›PMID 40593232›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

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

Chi Yen Tseng, Jessica A Salguero, Joshua D Breidenbach, Emilia Solomon, Claire K Sanders, Tara Harvey, M Grace Thornhill, Salvator J Palmisano, Zachary J Sasiene, Brett R Blackwell and 7 more

Abstract read
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In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
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  9. ThebioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Chi Yen TsengBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA. chiyen_tseng@lanl.gov.
Jessica A SalgueroBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Joshua D BreidenbachBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Emilia SolomonBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Claire K SandersMicrobial and Biome Sciences Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Tara HarveyBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
M Grace ThornhillBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Salvator J PalmisanoBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Zachary J SasieneBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Brett R BlackwellBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Ethan M McBrideBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Kes A LuchiniBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Erick S LeBrunLMI, Colorado Springs, CO, 80919, USA.
Marc AlvarezBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Phillip M MachBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Emilio S RiveraBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.
Trevor G GlarosBiochemistry and Biotechnology Group, Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, 84545, USA.

Funding

The Los Alamos National Laboratory Directed R&D Fund, LDRD 20230084DR
6 · The paper itself

Abstract

introductionData 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.

objectivesTo develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets.

methodsWe 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.

resultsProbabilistic 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.

conclusionOur 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.

Indexed as

LipidomicsMass SpectrometryMetabolomicsProteomicsHumansMachine LearningMultiomicsLipidomicsMetabolomicsMulti-omicsNormalizationProteomicsTime-course

Identifiers

PMID40593232
PMCPMC12214035

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.