Imperial College London
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ProfessorTimothyEbbels

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Professor of Biomedical Data Science
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

315DBurlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Wieder:2022:10.1101/2022.04.11.487976,
author = {Wieder, C and Lai, RPJ and Ebbels, T},
doi = {10.1101/2022.04.11.487976},
title = {Single sample pathway analysis in metabolomics: performance evaluation and application},
url = {http://dx.doi.org/10.1101/2022.04.11.487976},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:p>Single 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 thorough benchmark of established ssPA methods (ssGSEA, GSVA, SVD (PLAGE), and z-score) using semi-synthetic metabolomics data, alongside the evaluation of two novel methods we propose: ssClustPA and kPCA. While 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 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 <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://pypi.org/project/sspa/">https://pypi.org/project/sspa/</jats:ext-link>), providing implementations of all the methods benchmarked in this study. This 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.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>Pathway analysis is a computational method used to draw insights from omics data by identifying groups of molecules (biological pathways) whi
AU  - Wieder,C
AU  - Lai,RPJ
AU  - Ebbels,T
DO  - 10.1101/2022.04.11.487976
PY  - 2022///
TI  - Single sample pathway analysis in metabolomics: performance evaluation and application
UR  - http://dx.doi.org/10.1101/2022.04.11.487976
ER  -
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