McLuskey, K., Wandy, J. , Vincent, I. , Van Der Hooft, J. J.J. , Rogers, S. , Burgess, K. and Daly, R. (2021) Ranking metabolite sets by their activity levels. Metabolites, 11(2), 103. (doi: 10.3390/metabo11020103) (PMID:33670102) (PMCID:PMC7916825)
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Abstract
Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site.
| Item Type: | Articles |
|---|---|
| Additional Information: | R.D. and J.W. were funded by the Wellcome Trust (105614/Z/14/Z). KMcL was funded by Innovate UK (102511). J.J.J.v.d.H. was funded by an ASDI eScience grant, grant no. ASDI.2017.030, from the Netherlands eScience Center—NLeSC. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Vincent, Dr Isabel and Rogers, Dr Simon and Wandy, Dr Joe and McLuskey, Dr Karen and Van Der Hooft, Mr Justin and Burgess, Dr Karl and Daly, Dr Ronan |
| Creator Roles: | McLuskey, K.Methodology, Software, Writing – original draft, Writing – review and editing Wandy, J.Methodology, Software, Writing – original draft, Writing – review and editing Vincent, I.Validation, Writing – review and editing Van Der Hooft, J.Validation, Writing – review and editing Rogers, S.Conceptualization, Writing – review and editing Burgess, K.Writing – review and editing, Supervision Daly, R.Conceptualization, Methodology, Software, Writing – review and editing, Supervision |
| Authors: | McLuskey, K., Wandy, J., Vincent, I., Van Der Hooft, J. J.J., Rogers, S., Burgess, K., and Daly, R. |
| College/School: | College of Medical Veterinary and Life Sciences College of Medical Veterinary and Life Sciences > School of Molecular Biosciences College of Science and Engineering > School of Computing Science |
| Journal Name: | Metabolites |
| Publisher: | MDPI |
| ISSN: | 2218-1989 |
| ISSN (Online): | 2218-1989 |
| Published Online: | 11 February 2021 |
| Copyright Holders: | Copyright © 2021 The Authors |
| First Published: | First published in Metabolites 11(2):103 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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