ArticleMolecular & cellular proteomics : MCP2016
Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.
Article in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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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.
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Who cites it
43 citing papers in PubMed, 82 citations in OpenAlex.
- Heterogeneous antiretroviral drug distribution and HIV/SHIV detection in the gut of three species.Science translational medicine · 2019Trial
- TIMSImaging: an open and interoperable workflow for trapped ion mobility mass spectrometry imaging data processing and visualization.Bioinformatics (Oxford, England) · 2026Article
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Spatial Variation in Nodule Flavonoids of Soybean by Exogenous Nitrogen.Plant, cell & environment · 2026Article
- Spatially-guided metabolomics profiling of metabolic regions in human tumor tissues.Molecular systems biology · 2026Article
- Desorption Electrospray Ionization-Mass Spectrometry Imaging Provides Spatiochemical Information on Potential Biocontrol Agents againstJournal of the American Society for Mass Spectrometry · 2026Article
- Benchmarking Spatial Clustering Methods for Mass Spectrometry-Based Spatial Metabolomics.Metabolites · 2026Article
- Statistical Principles Define an Open-Source Differential Analysis Workflow for Mass Spectrometry Imaging Experiments with Complex Designs.bioRxiv : the preprint server for biology · 2026Article
- Intermittent fasting inhibits Tp53-driven glioma through gut microbiota-mediated methionine-mNature communications · 2026Article
- spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.Analytical chemistry · 2026Article
- Spatial metabolomic mapping reveals peel-color- and tissue-region-associated metabolic heterogeneity in postharvest 'Yanzhihong' apricot fruit.Frontiers in plant science · 2026Article
- LipidLocator: an open source Shiny web application for spatial lipidomics.Bioinformatics advances · 2026Article
- MSI.EAGLE: An Open-Source GUI for Streamlined Mass Spectrometry Imaging Analysis.bioRxiv : the preprint server for biology · 2025Article
- Disorder of phospholipid metabolism in the renal cortex and medulla contributes to acute tubular necrosis in mice after cantharidin exposure using integrative lipidomics and spatial metabolomics.Journal of pharmaceutical analysis · 2025Article
- The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.Journal of the American Society for Mass Spectrometry · 2024Article
- MSIreg: an R package for unsupervised coregistration of mass spectrometry and H&E images.Bioinformatics (Oxford, England) · 2024Article
- Metabolite Profiling of Hydroponic Lettuce Roots Affected by Nutrient Solution Flow: Insights from Comprehensive Analysis Using Widely Targeted Metabolomics and MALDI Mass Spectrometry Imaging Approaches.International journal of molecular sciences · 2024Article
- Review
- A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.Bioinformatics (Oxford, England) · 2023Article
- Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data.Analytical chemistry · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.
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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.