Year: 2022
Type: article
Abstract: Background 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 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. Results 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 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. Conclusion 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.
Source: BMC Bioinformatics
Authors: Cecilia Wieder, Rachel PJ Lai, Timothy Ebbels
Institution: Imperial College London
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: STEROID PROTECTION FROM TUBERCULOSIS-ASSOCIATED IMMUNE RECONSTITUTION INFLAMMATORY SYNDROME, G1502031688, Development of pathway-based predictive models and their use in multi-omics clinical studies, G1790795098, Standardised metabolite annotation workflows for enhanced biological interpretation in metabolomic data repositories, G2241581363, Development of pathway-based predictive models and their use in multi-omics clinical studies, Metabolomic Signatures of CAD Associated Genotypes, G2459364264, G3214677586, 2020BBSRC-NSF/BIO: Linking Mass Spectrometry Computational Ecosystems to Enhance Biological Insights of Publicly-Available Data, Standardised metabolite annotation workflows for enhanced biological interpretation in metabolomic data repositories, G6841377425, G9000127672, G9000341200
OpenAlex ID: W4308948103