Cluster Analysis of Untargeted Metabolomic Experiments

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.