Edoardo Gaude, Francesca Chignola, Dimitrios Spiliotopoulos, Andrea Spitaleri, Michela Ghitti, Jose M Garcia-Manteiga, Silvia Mari and Giovanna Musco Pages 180 - 189 ( 10 )
Metabolomics, similarly to other high-throughput “-omics” techniques, generates large arrays of data, whose analysis and interpretation can be difficult and not always straightforward. Several software for the detailed metabolomics statistical analysis are available, however there is a lack of simple protocols guiding the user through a standard statistical analysis of the data.
Herein we present “muma”, an R package providing a simple step-wise pipeline for metabolomics univariate and multivariate statistical analyses. Based on published statistical algorithms and techniques, muma provides user-friendly tools for the whole process of data analysis, ranging from data imputation and preprocessing, to dataset exploration, to data interpretation through unsupervised/supervised multivariate and/or univariate techniques. Of note, specific tools and graphics aiding the explanation of statistical outcomes have been developed. Finally, a section dedicated to metabolomics data interpretation has been implemented, providing specific techniques for molecular assignments and biochemical interpretation of metabolic patterns.
muma is a free, user-friendly and versatile tool suite tailored to assist the user in the interpretation of metabolomics data in the identification of biomarkers and in the analysis of metabolic patterns.
Chemometrics, metabonomics, metabolic pattern, multivariate analysis, R package, statistical analysis, univariate analysis.
Dulbecco Telethon Institute, Biomolecular NMR Laboratory c/o Center for Translational Genomics and Bioinformatics, Ospedale San Raffaele, Milano, Italy.