ArticleBMC bioinformatics2022
Single sample pathway analysis in metabolomics: performance evaluation and application.
Article in BMC bioinformatics, 2022. 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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Who cites it
20 citing papers in PubMed.
- Network-based integration of metabolomics data from large-scale repositories.Metabolomics : Official journal of the Metabolomic Society · 2026Article
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- mFLIP: metabolic flux interval prediction.BMC bioinformatics · 2026Article
- ssNetShift: single-sample metabolic network rewiring reveals hidden prognostic subtypes beyond clinical staging in gastric cancer.Briefings in bioinformatics · 2026Article
- Long COVID involves activation of proinflammatory and immune exhaustion pathways.Nature immunology · 2026Observational
- Simulated metabolic profiles reveal biases in pathway analysis methods.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Longitudinal NMR-based Metabolomics Analysis of Male Mountain Ultramarathon Runners: New Perspectives for Athletes Monitoring and Injury Prevention.Sports medicine - open · 2025Article
- Phoenics: a novel statistical approach for longitudinal metabolomic pathway analysis.BMC bioinformatics · 2025Article
- A computational framework for detecting inter-tissue gene-expression coordination changes with aging.Scientific reports · 2025Article
- Synthetic data generation methods in healthcare: A review on open-source tools and methods.Computational and structural biotechnology journal · 2024Review
- Nucleotide sugars correlate with leukocyte telomere length as part of a dyskeratosis congenita metabolomic plasma signature.Haematologica · 2024Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- The metabolic role of vitamin D in children's neurodevelopment: a network study.Scientific reports · 2024Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.PLoS computational biology · 2024Article
- PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration.bioRxiv : the preprint server for biology · 2024Article
- Effects of a second iron-dextran injection administered to piglets during lactation on differential gene expression in liver and duodenum at weaning.Journal of animal science · 2024Article
- Recent advances in mass spectrometry-based computational metabolomics.Current opinion in chemical biology · 2023Review
- The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises.Computational and structural biotechnology journal · 2023Review
- Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information.Genome biology · 2022Article
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3 authors.
Funding
Abstract
backgroundSingle sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures.
resultsWhile GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at https://pypi.org/project/sspa/ ), providing implementations of all the methods benchmarked in this study.
conclusionThis work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.
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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 Socratic graph.