As part of a systems biology approach, metabolomics often aim at broadening our understanding of the functionality of biological systems as a whole. Observations from stand-alone experiments may reveal interesting changes in metabolites of a specific pathway or metabolite class. However, bringing these observations into context with more general biological processes requires the integration and comparison of different datasets. This chapter aims at introducing and explaining methods of comparative data analysis for plant metabolomics using the statistical software framework R.
Keywords: Clustering; Comparative analysis; Correlation; Data integration; R programing language; R software packages; Summary statistics; Systems biology.