ArticleScientific reports2019
Metrics for evaluating the stability and reproducibility of mass spectra.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Dimensional cophenetic integrity: a method for evaluation of dimensionality reduction in MSI.Bioinformatics advances · 2026Article
- Topological segmentation of mass spectrometry imaging data.Journal of mass spectrometry and advances in the clinical lab · 2025Article
- Differential Mobility Spectrometry Authenticates the Botanical Origins of Five Monofloral Honeys.Journal of agricultural and food chemistry · 2025Article
- Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling.Molecules (Basel, Switzerland) · 2022Article
- Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues.Mass spectrometry (Tokyo, Japan) · 2021Article
- Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification.Nature communications · 2020Article
- Inline cartridge extraction for rapid brain tumor tissue identification by molecular profiling.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, we demonstrate a new approach for assessing the stability and reproducibility of mass spectra obtained via ambient ionization methods. This method is suitable for both comparing experiments during which only one mass spectrum is measured and for evaluating the internal homogeneity of mass spectra collected over a period of time. The approach uses Pearson's r coefficient and the cosine measure to compare the spectra. It is based on the visualization of dissimilarities between measurements, thus leading to the analysis of dissimilarity patterns. The cosine measure and correlations are compared to obtain better metrics for spectra homogeneity. The method filters out unreliable scans to prevent the analyzed sample from being wrongly characterized. The applicability of the method is demonstrated on a set of brain tumor samples. The developed method could be employed in neurosurgical applications, where mass spectrometry is used to monitor the intraoperative tumor border.
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Registered trials
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