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Cluster Analysis of Untargeted Metabolomic Experiments

  • Joshua HeinemannEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1859)

Abstract

Untargeted metabolite profiling based upon LC-MS methodology can be used to identify unique metabolic phenotypes associated with stress, disease or environmental exposure of cells using mathematical clustering. Here, we show how unsupervised data analysis is a powerful tool for both quality control and answering simple biological questions. We will demonstrate how to format untargeted mass spectrometry data for import into R, a programming language and software environment for statistical computing (R Development Core Team. R: A language and environment for statistical computing, reference index version 2.15. R Foundation for Statistical Computing, Vienna, 2012). Using R, we transform untargeted metabolite data using hierarchical clustering and principal component analysis (PCA) to create visual representations of change between biological samples and explore how these can be used predictively, in determining environmental stress, health and metabolic insight.

Key words

Clustering Cluster analysis Pattern recognition Untargeted metabolomics Phenotyping Data mining 

Notes

Acknowledgments

The authors would also like to acknowledge that this work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the US Department of Energy.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Environmental Genomics and Systems BiologyLawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.Joint BioEnergy InstituteEmeryvilleUSA

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