ArticleBioinformatics (Oxford, England)2018
Missing value imputation for LC-MS metabolomics data by incorporating metabolic network and adduct ion relations.
Article in Bioinformatics (Oxford, England), 2018. 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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Who cites it
13 citing papers in PubMed.
- Drought modulates ozone stress through BVOCs, antioxidant defenses, and metabolic responses in a tropical tree.Planta · 2026Article
- A deep learning architecture for combining and imputing heterogeneous metabolomics datasets.BMC bioinformatics · 2026Article
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- BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.Journal of proteome research · 2023Article
- Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data.Metabolites · 2022Article
- Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease.Metabolites · 2021Article
- Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis.Frontiers in oncology · 2021Review
- Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine.Metabolites · 2020Review
- GMSimpute: a generalized two-step Lasso approach to impute missing values in label-free mass spectrum analysis.Bioinformatics (Oxford, England) · 2020Article
- The missing indicator approach for censored covariates subject to limit of detection in logistic regression models.Annals of epidemiology · 2019Article
- Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses.International journal of molecular sciences · 2019Review
- Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome.Metabolites · 2018Article
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Authors and funding
3 authors.
Funding
Abstract
Motivation: Metabolomics data generated from liquid chromatography-mass spectrometry platforms often contain missing values. Existing imputation methods do not consider underlying feature relations and the metabolic network information. As a result, the imputation results may not be optimal. Results: We proposed an imputation algorithm that incorporates the existing metabolic network, adduct ion relations even for unknown compounds, as well as linear and nonlinear associations between feature intensities to build a feature-level network. The algorithm uses support vector regression for missing value imputation based on features in the neighborhood on the network. We compared our proposed method with methods being widely used. As judged by the normalized root mean squared error in real data-based simulations, our proposed methods can achieve better accuracy. Availability and implementation: The R package is available at http://web1.sph.emory.edu/users/tyu8/MINMA. Contact: jiankang@umich.edu or tianwei.yu@emory.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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