Orbitrap noise structure and method for noise unbiased multivariate analysis

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Michael R Keenan Gustavo F Trindade Alexander Pirkl Clare Newell Yuhong Jin Konstantin Aizikov Andreas Dannhorn Junting Zhang Lidija Matjačić Henrik Arlinghaus Anya Eyres Rasmus Havelund Richard JA Goodwin Zoltan Takats Josephine Bunch Alex Gould Alexander Makarov Ian S Gilmore
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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.

Journal details

Journal Nature Communications
Volume 16
Issue number 1
Pages 6398
Available online
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Publisher website (DOI) 10.1038/s41467-025-61542-2
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Europe PubMed Central 40640123
Pubmed 40640123

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The publication was previously a preprint. View preprint