Orbitrap noise structure and method for noise unbiased multivariate analysis

Michael R. Keenan, Gustavo F. Trindade, Alexander Pirkl, Clare L. Newell, Yuhong Jin, Konstantin Aizikov, Andreas Dannhorn, Junting Zhang, Lidija Matjačić, Henrik Arlinghaus, Anya Eyres, Rasmus Havelund, Richard J. A. Goodwin, Zoltan Takats, Josephine Bunch, Alex P. Gould, Alexander Makarov and Ian S. Gilmore ()
Additional contact information
Michael R. Keenan: Independent
Gustavo F. Trindade: NiCE-MSI
Alexander Pirkl: IONTOF GmbH
Clare L. Newell: The Francis Crick Institute
Yuhong Jin: The Francis Crick Institute
Konstantin Aizikov: Thermo Fisher Scientific
Andreas Dannhorn: AstraZeneca
Junting Zhang: NiCE-MSI
Lidija Matjačić: NiCE-MSI
Henrik Arlinghaus: IONTOF GmbH
Anya Eyres: NiCE-MSI
Rasmus Havelund: NiCE-MSI
Richard J. A. Goodwin: AstraZeneca
Zoltan Takats: Imperial College London
Josephine Bunch: NiCE-MSI
Alex P. Gould: The Francis Crick Institute
Alexander Makarov: Thermo Fisher Scientific
Ian S. Gilmore: NiCE-MSI

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Orbitrap mass spectrometry is widely used in the life-sciences. However, like all mass spectrometers, non-uniform (heteroscedastic) noise introduces bias in multivariate analysis complicating data interpretation. Here, we study the noise structure of an Orbitrap mass analyser integrated into a secondary ion mass spectrometer (OrbiSIMS). Using a stable primary ion beam to provide a well-controlled source of ions from a silver sample, we find that noise has three characteristic regimes: at low signals the Orbitrap detector noise and a censoring algorithm dominates; at intermediate signals counting noise specific to the ion emission process is most significant; and at high signals additional sources of measurement variation become important. Using this understanding, we developed a generative model for Orbitrap data that accounts for the noise distribution and introduce a scaling method, termed WSoR, to reduce the effects of noise bias in multivariate analysis. We compare WSoR performance with no-scaling and existing scaling methods for three biological imaging data sets including drosophila central nervous system, mouse testis and a desorption electrospray ionisation (DESI) image of a rat liver. WSoR consistently performed best at discriminating chemical information from noise. The performance of the other methods varied on a case-by-case basis, complicating the analysis.

Date: 2025
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DOI: 10.1038/s41467-025-61542-2

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