ArticleScientific reports2022
Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI).
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactiveScience advances · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Identification of precursor origins of synthetic graphite anodes in lithium-ion batteries using LDI-MS and multivariate analysis.Nano convergence · 2026Article
- 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
- MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation.Journal of proteome research · 2025Article
- Spatially Informed Nonnegative Matrix Trifactorization for Coclustering Mass Spectrometry Data.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Mass-Guided Single-Cell MALDI Imaging of Low-Mass Metabolites Reveals Cellular Activation Markers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Article
- Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research.Trends in analytical chemistry : TRAC · 2023Article
- Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics.Analytical chemistry · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. In summary, the extraction of spatial patterns from MSI data can be done using different unsupervised methods, but the robust evaluation of clustering results is what is still missing. In this study, we have performed multiple simulations on synthetic and real MSI data to validate the performance of unsupervised methods. The synthetic data were simulated mimicking important spatial and statistical properties of real MSI data. Our simulation results confirmed that K-means clustering with correlation distance and Gaussian Mixture Modeling clustering methods give optimal performance in most of the scenarios. The clustering methods give efficient results together with dimension reduction techniques. From all the dimension techniques considered here, the best results were obtained with the minimum noise fraction (MNF) transform. The results were confirmed on both synthetic and real MSI data. However, for successful implementation of MNF transform the MSI data requires to be of limited dimensions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.