MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

Martin J. Rusilowicz; Michael Dickinson; Adrian J. Charlton; Simon O’Keefe; Julie Wilson

doi:10.1371/journal.pone.0205968

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways

Martin J. Rusilowicz, Michael Dickinson, Adrian J. Charlton, Simon O’Keefe, Julie Wilson

Research output: Contribution to journal › Article › peer-review

Abstract

Motivation Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance. Results We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

Original language	English
Article number	e0205968
Journal	PLoS ONE
Volume	13
Issue number	10
DOIs	https://doi.org/10.1371/journal.pone.0205968
Publication status	Published - 29 Oct 2018

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© 2018 Rusilowicz et al.
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Cite this

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title = "MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways",

abstract = "Motivation Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance. Results We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.",

author = "Rusilowicz, {Martin J.} and Michael Dickinson and Charlton, {Adrian J.} and Simon O{\textquoteright}Keefe and Julie Wilson",

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AU - Rusilowicz, Martin J.

AU - Dickinson, Michael

AU - Charlton, Adrian J.

AU - O’Keefe, Simon

AU - Wilson, Julie

PY - 2018/10/29

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N2 - Motivation Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance. Results We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

AB - Motivation Modern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance. Results We present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

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