Method Article

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

DOI:

10.3791/65512

November 10th, 2023

In This Article

Summary

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We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.

Abstract

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A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Introduction

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In the last decade, metabolomics has emerged as an omics science due to advances in analytical technologies such as Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS). These techniques allow simultaneous measurement of hundreds to thousands of small molecule metabolites, creating complex multidimensional datasets. Metabolomics experiments can be performed in targeted or untargeted modes. Targeted metabolomics experiments measure specific classes of metabolites. They are usually hypothesis-driven, while untargeted approaches attempt to measure as many metabolites as possible and are hypothesis-generating in nature. Targeted....

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Protocol

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1. CorrelationCalculator

  1. Download a sample comma-delimited input file containing a list of metabolites with experimental measurements at http://metscape.med.umich.edu/kora_data_240.csv.
  2. Double-click on the downloaded sample file to open it.
    1. Ensure that the file contains labels for both the samples and the metabolites.
    2. Since samples are in rows, confirm that the first column is the sample names and the first row is the metabolite names.
  3. Download the CorrelationCalculator Java application (http://metscape.med.umich.edu/calculator.html). Double-click on the downloaded .jar file to ....

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Results

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To illustrate the use of CorrelationCalculator, we constructed a partial correlation network using a subset of the metabolomics data from the KORA population study described in Krumsiek et al.24. The dataset contained 151 metabolites and 240 samples. Figure 1 shows the resulting partial correlation network that was visualized in Cytoscape. The network contains 148 nodes and 272 edges. The color of the nodes represents metabolites that belong to different chem.......

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Discussion

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Partial correlation-based network analysis methods implemented in CorrelationCalculator and Filigree help overcome some of the limitations of knowledge-based metabolic pathway analyses, especially for the datasets with a high prevalence of unknown metabolites and limited coverage of metabolic pathways (e.g., lipidomics data). These tools have been widely used by the research community to analyze a broad range of metabolomics and lipidomics data14,22,

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Disclosures

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The authors have no competing financial interests.

Acknowledgements

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This work was supported by NIH 1U01CA235487 grant.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CorrelationCalculatorJAVAhttp://metscape.med.umich.edu/calculator.html
clusterNethttps://github.com/Karnovsky-Lab/clusterNet
CytoscapeCytoscapehttps://cytoscape.org/
FiligreeJAVAhttp://metscape.med.umich.edu/filigree.html
MetScapeCytoscapehttps://apps.cytoscape.org/apps/metscapeCytoscape application that allows for the creation and exploration of correlation networks.

References

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  1. Sas, K. M., Karnovsky, A., Michailidis, G., Pennathur, S. Metabolomics and diabetes: analytical and computational approaches. Diabetes. 64 (3), 718-732 (2015).
  2. Cottret, L., et al. MetExplore: Collaborative edition and exploration of metabolic networks. ....

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Tags

Metabolomics DataNetwork AnalysisCorrelation NetworksPartial CorrelationDifferential NetworkEnrichment AnalysisMetabolomics ToolsLC MS AnalysisLipid MetabolismPathway Enrichment

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