ArticleAnalytical and bioanalytical chemistry2021
Spatially aware clustering of ion images in mass spectrometry imaging data using deep learning.
Article in Analytical and bioanalytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactiveScience advances · 2026Article
- spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.Analytical chemistry · 2026Article
- Topological segmentation of mass spectrometry imaging data.Journal of mass spectrometry and advances in the clinical lab · 2025Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- Recent Applications of Artificial Intelligence and Related Technical Challenges in MALDI MS and MALDI-MSI: A Mini Review.Mass spectrometry (Tokyo, Japan) · 2025Review
- [Research progress of deep learning applications in mass spectrometry imaging data analysis].Se pu = Chinese journal of chromatography · 2024Review
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Mass spectrometry imaging methods for visualizing tumor heterogeneity.Current opinion in biotechnology · 2024Review
- DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.Analytical chemistry · 2024Article
- Article
- rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation.Journal of cheminformatics · 2023Article
- Prospective on Imaging Mass Spectrometry in Clinical Diagnostics.Molecular & cellular proteomics : MCP · 2023Review
- Review
- A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images.Bioinformatics (Oxford, England) · 2023Article
- DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data.BMC bioinformatics · 2022Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Identifying multicellular spatiotemporal organization of cells with SpaceFlow.Nature communications · 2022Article
- A Neural Network-Based Method for Fast Capture and Tracking of Laser Links between Nonorbiting Platforms.Computational intelligence and neuroscience · 2022Article
- Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial.Journal of mass spectrometry and advances in the clinical lab · 2022Article
- Self-supervised clustering of mass spectrometry imaging data using contrastive learning.Chemical science · 2021Article
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7 authors.
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Abstract
Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, is used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the relative isotope ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.
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