ArticleMolecules (Basel, Switzerland)2021
NMF-Based Approach for Missing Values Imputation of Mass Spectrometry Metabolomics Data.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.BMC bioinformatics · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 2025Article
- Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.Frontiers in psychiatry · 2025Article
- Optimizing multi-omics data imputation with NMF and GAN synergy.Bioinformatics (Oxford, England) · 2024Article
- Evaluating Proteomics Imputation Methods with Improved Criteria.Journal of proteome research · 2023Article
- Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions.medRxiv : the preprint server for health sciences · 2023Article
- Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence.Translational psychiatry · 2023Article
- MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization.Genome biology · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
In mass spectrometry (MS)-based metabolomics, missing values (NAs) may be due to different causes, including sample heterogeneity, ion suppression, spectral overlap, inappropriate data processing, and instrumental errors. Although a number of methodologies have been applied to handle NAs, NA imputation remains a challenging problem. Here, we propose a non-negative matrix factorization (NMF)-based method for NA imputation in MS-based metabolomics data, which makes use of both global and local information of the data. The proposed method was compared with three commonly used methods: k-nearest neighbors (kNN), random forest (RF), and outlier-robust (ORI) missing values imputation. These methods were evaluated from the perspectives of accuracy of imputation, retrieval of data structures, and rank of imputation superiority. The experimental results showed that the NMF-based method is well-adapted to various cases of data missingness and the presence of outliers in MS-based metabolic profiles. It outperformed kNN and ORI and showed results comparable with the RF method. Furthermore, the NMF method is more robust and less susceptible to outliers as compared with the RF method. The proposed NMF-based scheme may serve as an alternative NA imputation method which may facilitate biological interpretations of metabolomics data.
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