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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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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