Ranking metabolite sets by their activity levels

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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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.
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 > Institute of Molecular Cell and Systems Biology
College of Science and Engineering > School of Computing Science
Journal Name:Metabolites
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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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172121Funding SchemesAnna DominiczakWellcome Trust (WELLCOTR)105614/Z/14/ZInstitute of Cardiovascular & Medical Sciences
172730Enhanced interpretation of metabolomics data to accelerate microbial engineeringKarl BurgessInnovate UK (INNOVATE)TS/N006798/1 - 102511Institute of Infection, Immunity & Inflammation