Orbitrap noise structure and method for noise unbiased multivariate analysis Article Swipe
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
,
Zoltán Takáts
,
Josephine Bunch
,
Alex P. Gould
,
Alexander Makarov
,
Ian S. Gilmore
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-61542-2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41467-025-61542-2
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41467-025-61542-2
- https://www.nature.com/articles/s41467-025-61542-2.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412166207
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412166207Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41467-025-61542-2Digital Object Identifier
- Title
-
Orbitrap noise structure and method for noise unbiased multivariate analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-07-10Full publication date if available
- Authors
-
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, Zoltán Takáts, Josephine Bunch, Alex P. Gould, Alexander Makarov, Ian S. GilmoreList of authors in order
- Landing page
-
https://doi.org/10.1038/s41467-025-61542-2Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41467-025-61542-2.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41467-025-61542-2.pdfDirect OA link when available
- Concepts
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Noise (video), Multivariate statistics, Computer science, Multivariate analysis, Orbitrap, Statistics, Mathematics, Artificial intelligence, Machine learning, Chemistry, Mass spectrometry, Chromatography, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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