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

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

SUBMITTER: Eardley-Brunt ASJ 

PROVIDER: S-EPMC7618781 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature