Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.
Keywords: Bioinformatics tools; Data normalization; Multivariate statistics; Quality control; R programing language; R software packages; Untargeted metabolomics.