ArticleAnalytical chemistry2021
Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 60 citations in OpenAlex.
- 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
- Towards precision oncology: unsupervised manifold learning for spatial molecular profiling in cancer tissues.BMC bioinformatics · 2026Article
- Mass spectrometry imaging tutorial: From cancer biomarker discovery to clinical applications.Analytica chimica acta · 2026Review
- spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.Analytical chemistry · 2026Article
- NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.Bioinformatics (Oxford, England) · 2026Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- Resolving spatiotemporal dynamics in bacterial multicellular populations: approaches and challenges.Microbiology and molecular biology reviews : MMBR · 2025Review
- Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.Nature communications · 2025Article
- Article
- An Effective Approach to Mass Spectrometry Imaging Data Partitioning Using UMAP and k-Means Clustering.Mass spectrometry (Tokyo, Japan) · 2025Article
- Mass Spectrometry Imaging for Spatial Ingredient Classification in Plant-Based Food.Journal of the American Society for Mass Spectrometry · 2025Article
- Processing Next-Generation Mass Spectrometry Imaging Data: Principal Component Analysis at Scale.Journal of the American Society for Mass Spectrometry · 2024Article
- Extended similarity methods for efficient data mining in imaging mass spectrometry.Digital discovery · 2024Article
- Next Generation of Mass Spectrometry Imaging: from Micrometer to Subcellular Resolution.Chemical & biomedical imaging · 2023Review
- Multimodal high-resolution nano-DESI MSI and immunofluorescence imaging reveal molecular signatures of skeletal muscle fiber types.Chemical science · 2023Article
- Analysis of Metabolite Distribution in Rat Liver of High-Fat Model by Mass Spectrometry Imaging.Metabolites · 2023Article
- A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.Bioinformatics (Oxford, England) · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- High-Throughput Nano-DESI Mass Spectrometry Imaging of Biological Tissues Using an Integrated Microfluidic Probe.Analytical chemistry · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions, providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive, as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis were treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map was assembled from segment candidates that were generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections that were acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.
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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.