ArticleBMC bioinformatics2010
Probabilistic principal component analysis for metabolomic data.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.
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Who cites it
67 citing papers in PubMed.
- MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.BMC bioinformatics · 2013Trial
- Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.Sensors (Basel, Switzerland) · 2026Review
- Alterations in gut microbiota and fecal metabolites in euthyroid autoimmune thyroiditis during early pregnancy.Journal of translational internal medicine · 2026Article
- Non-targeted Metabolomics Reveals the Potential Role of MFSD8 in Metabolism in Human Endothelial Cells.Molecular biotechnology · 2026Article
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- High-Resolution LC-MS Characterization ofToxins · 2026Article
- Altered lipidomic and metabolomic status in cerebrospinal fluid of children with myelin oligodendrocyte glycoprotein antibody-associated disorder.Biochemistry and biophysics reports · 2025Article
- Profiling Environmental Variations in Condensed Tannins and Other Metabolites of Birdsfoot Trefoil (Plants (Basel, Switzerland) · 2025Article
- Simplex-structured matrix factorisation: application of soft clustering to metabolomic data.Scientific reports · 2025Article
- Discovery of SARS-CoV-2 Nsp14-Methyltransferase (MTase) Inhibitors by Harnessing Scaffold-Centric Exploration of the Ultra Large Chemical Space.ACS pharmacology & translational science · 2025Article
- Matrix Linear Models for Connecting Metabolite Composition to Individual Characteristics.Metabolites · 2025Article
- Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.Journal of chemical information and modeling · 2025Article
- Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data.Computational and structural biotechnology journal · 2024Article
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- Multi-omics profiling reveals the molecular mechanisms of HBMC genomics · 2024Article
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- Label-Free Quantitation of Endogenous Peptides.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Matrix Linear Models for connecting metabolite composition to individual characteristics.bioRxiv : the preprint server for biology · 2023Article
- Ultra-high Performance Liquid Chromatography-Ion Mobility-High-Resolution Mass Spectrometry to Evaluate the Metabolomic Response of Durum Wheat to Sustainable Treatments.Journal of agricultural and food chemistry · 2023Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model.
resultsHere, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data.
conclusionsThe methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.
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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.