ArticleRapid communications in mass spectrometry : RCM2021
A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data.
Article in Rapid communications in mass spectrometry : RCM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- Recovering missing features in nonnegative matrix factorization via generalized singular value decomposition.iScience · 2026Article
- Non-negative matrix factorization and deconvolution as a dual simplex problem.Genome biology · 2026Article
- Millimetre-Scale Stratification of Microbial Communities in Hydrothermal Sediments.Environmental microbiology · 2026Article
- Unmixing of Imaging Mass Spectrometry Measurements Using Microscopy-Informed Constraints.bioRxiv : the preprint server for biology · 2025Article
- Advanced Dimensionality Reduction for Imaging Mass Spectrometry of Human Eye Tissue through Low-Rank Modeling with Sparse and Dense Residuals.Analytical chemistry · 2025Article
- NMFProfiler: a multi-omics integration method for samples stratified in groups.Bioinformatics (Oxford, England) · 2025Article
- Adipose tissue composition determines its computed tomography radiodensity.European radiology · 2024Article
- Article
- Spatiochemical Characterization of the Pancreas Using Mass Spectrometry Imaging and Topological Data Analysis.Analytical chemistry · 2023Article
- Automated Library Generation and Serendipity Quantification Enables Diverse Discovery in Coordination Chemistry.Journal of the American Chemical Society · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- A mathematical comparison of non-negative matrix factorization related methods with practical implications for the analysis of mass spectrometry imaging data.Rapid communications in mass spectrometry : RCM · 2021Article
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Abstract
rationaleNon-negative matrix factorization (NMF) has been used extensively for the analysis of mass spectrometry imaging (MSI) data, visualizing simultaneously the spatial and spectral distributions present in a slice of tissue. The statistical framework offers two related NMF methods: probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), which is a generative model. This work offers a mathematical comparison between NMF, PLSA, and LDA, and includes a detailed evaluation of Kullback-Leibler NMF (KL-NMF) for MSI for the first time. We will inspect the results for MSI data analysis as these different mathematical approaches impose different characteristics on the data and the resulting decomposition.
methodsThe four methods (NMF, KL-NMF, PLSA, and LDA) are compared on seven different samples: three originated from mice pancreas and four from human-lymph-node tissues, all obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).
resultsWhere matrix factorization methods are often used for the analysis of MSI data, we find that each method has different implications on the exactness and interpretability of the results. We have discovered promising results using KL-NMF, which has only rarely been used for MSI so far, improving both NMF and PLSA, and have shown that the hitherto stated equivalent KL-NMF and PLSA algorithms do differ in the case of MSI data analysis. LDA, assumed to be the better method in the field of text mining, is shown to be outperformed by PLSA in the setting of MALDI-MSI. Additionally, the molecular results of the human-lymph-node data have been thoroughly analyzed for better assessment of the methods under investigation.
conclusionsWe present an in-depth comparison of multiple NMF-related factorization methods for MSI. We aim to provide fellow researchers in the field of MSI a clear understanding of the mathematical implications using each of these analytical techniques, which might affect the exactness and interpretation of the results.
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