Ranking Metabolite Sets by Their Activity Levels

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 experim...

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Main Authors: Karen McLuskey, Joe Wandy, Isabel Vincent, Justin J. J. van der Hooft, Simon Rogers, Karl Burgess, Rónán Daly
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Metabolites
Subjects:
SVD
Online Access:https://www.mdpi.com/2218-1989/11/2/103
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spelling doaj-40d74266744b457090fdf595ad240f592021-02-12T00:00:28ZengMDPI AGMetabolites2218-19892021-02-011110310310.3390/metabo11020103Ranking Metabolite Sets by Their Activity LevelsKaren McLuskey0Joe Wandy1Isabel Vincent2Justin J. J. van der Hooft3Simon Rogers4Karl Burgess5Rónán Daly6Glasgow Polyomics, University of Glasgow, Glasgow G61 1QH, UKGlasgow Polyomics, University of Glasgow, Glasgow G61 1QH, UKIBioIC, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G1 1XQ, UKBioinformatics Group, Wageningen University, 6708 PB Wageningen, The NetherlandsSchool of Computing Science, University of Glasgow, Glasgow G12 8QQ, UKCentre for Synthetic and Systems Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JG, UKGlasgow Polyomics, University of Glasgow, Glasgow G61 1QH, UKRelated 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.https://www.mdpi.com/2218-1989/11/2/103liquid chromatography–mass spectrometry (LC/MS)pathwaysmolecular familyMass2MotifSVDmatrix decomposition
collection DOAJ
language English
format Article
sources DOAJ
author Karen McLuskey
Joe Wandy
Isabel Vincent
Justin J. J. van der Hooft
Simon Rogers
Karl Burgess
Rónán Daly
spellingShingle Karen McLuskey
Joe Wandy
Isabel Vincent
Justin J. J. van der Hooft
Simon Rogers
Karl Burgess
Rónán Daly
Ranking Metabolite Sets by Their Activity Levels
Metabolites
liquid chromatography–mass spectrometry (LC/MS)
pathways
molecular family
Mass2Motif
SVD
matrix decomposition
author_facet Karen McLuskey
Joe Wandy
Isabel Vincent
Justin J. J. van der Hooft
Simon Rogers
Karl Burgess
Rónán Daly
author_sort Karen McLuskey
title Ranking Metabolite Sets by Their Activity Levels
title_short Ranking Metabolite Sets by Their Activity Levels
title_full Ranking Metabolite Sets by Their Activity Levels
title_fullStr Ranking Metabolite Sets by Their Activity Levels
title_full_unstemmed Ranking Metabolite Sets by Their Activity Levels
title_sort ranking metabolite sets by their activity levels
publisher MDPI AG
series Metabolites
issn 2218-1989
publishDate 2021-02-01
description 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.
topic liquid chromatography–mass spectrometry (LC/MS)
pathways
molecular family
Mass2Motif
SVD
matrix decomposition
url https://www.mdpi.com/2218-1989/11/2/103
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