ArticleJournal of mass spectrometry : JMS2022
Sequential paired covariance for improved visualization of mass spectrometry imaging datasets.
Article in Journal of mass spectrometry : JMS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Resolving the glycosaminoglycan signature of ischemic stroke brain using PRM-based IR-MALDESI mass spectrometry imaging.Analytical and bioanalytical chemistry · 2026Article
- Comparison of Mid-Infrared and Ultraviolet Lasers Coupled to the MALDESI Source for the Detection of Secondary Metabolites and Structural Lipids in Arabidopsis thaliana.Journal of mass spectrometry : JMS · 2025Article
- Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.Nature communications · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Sequential paired covariance for improved visualization of mass spectrometry imaging datasets.Journal of mass spectrometry : JMS · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
Untargeted analyses in mass spectrometry imaging produce hundreds of ion images representing spatial distributions of biomolecules in biological tissues. Due to the large diversity of ions detected in untargeted analyses, normalization standards are often difficult to implement to account for pixel-to-pixel variability in imaging studies. Many normalization strategies exist to account for this variability, but they largely do not improve image quality. In this study, we present a new approach for improving image quality and visualization of tissue features by application of sequential paired covariance (SPC). This approach was demonstrated using previously published tissue datasets such as rat brain and human prostate with different biomolecules like metabolites and N-linked glycans. Data transformation by SPC improved ion images resulting in increased smoothing of biological features compared with commonly used normalization approaches.
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