Optimising the choice of normalisation method for use in machine-learning classification of human blood plasma ambient ionisation mass spectra.

Annabel S J Eardley-Brunt, Liwen Song, Oxford Acute Myocardial Infarction Study, Oxford Abdominal Aortic Aneurysm Study, Claire Vallance

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Article in International journal of mass spectrometry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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3 · Its place in the literature

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1 citing paper in PubMed.

Prediction of clinical outcomes of ST-elevated myocardial infarction patients using atmospheric solids analysis probe mass spectrometry and machine learning.The Analyst · 2025
Article

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5 · Who and what money

Authors and funding

5 authors.

Annabel S J Eardley-BruntDepartment of Chemistry, University of Oxford, Chemistry Research Laboratory, 12 Mansfield Rd, Oxford OX1 3TA, UK.ORCID https://orcid.org/0000-0002-8815-6768

Liwen SongDepartment of Chemistry, University of Oxford, Chemistry Research Laboratory, 12 Mansfield Rd, Oxford OX1 3TA, UK.

Oxford Acute Myocardial Infarction Study

Oxford Abdominal Aortic Aneurysm Study

Claire VallanceDepartment of Chemistry, University of Oxford, Chemistry Research Laboratory, 12 Mansfield Rd, Oxford OX1 3TA, UK.ORCID https://orcid.org/0000-0003-3880-8614

Funding

British Heart Foundation CH/16/1/32013British Heart Foundation RE/13/1/30181Wellcome Trust 218514

6 · The paper itself

Abstract

Construction of large mass spectrometric data sets usually involves some combination of normalisation, scaling, and transformation of individual mass spectra in order to correct for technical (and sometimes biological) variation. Many different approaches to data normalisation have been reported, and there is no particular consensus on the best approach. The present study systematically evaluates a set of 24 normalisation, scaling, and transformation methods, and their 420 possible combinations, in the context of atmospheric solids analysis probe (ASAP) mass spectra of human blood plasma. The plasma samples came from two separate cohorts of patients, enrolled respectively in the Oxford Acute Myocardial Infarction (OxAMI) and Oxford Abdominal Aortic Aneurysm (OxAAA) clinical studies. Within each cohort, patients are classified according to a number of different clinical variables. We have investigated the effect of normalisation, scaling, and transformation method on subsequent clustering of the data into the classes of interest, and on machine-learning based classification of the data into the categories of interest. The choice of method was found to have a substantial effect on data clustering, measured via the clustering ratio

Indexed as

Atmospheric solids analysis probe massClustering ratioData normalisationMachine learningMultivariate analysisPreprocessingspectrometry (ASAP-MS)

Identifiers

PMID41736891

DOI10.1016/j.ijms.2025.117553

PMCPMC7618781

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