The several modules that MetaboAnalyst 3.0 has are presented as following:
- Statistical analysis: This module offers various commonly used statistical and machine learning methods including t-tests, ANOVA, PCA, PLS-DA and Orthogonal PLS-DA. It also provides clustering and visualization tools to create dendrograms and heatmaps as well as to classify based on random forests and SVM.
- Functional enrichment analysis: The service performs metabolite set enrichment analysis (MSEA) for human and mammalian species. The analysis is based on several libraries containing ~6300 groups of biologically meaningful metabolite sets collected primarily from human studies
- Metabolic pathway analysis: The service currently supports pathway analysis (including pathway enrichment analysis and pathway topology analysis) and visualization for 21 model organisms, including Human, Mouse, Rat, Cow, Chicken, Zebrafish, Arabidopsis thaliana, Rice, Drosophila, Malaria, Budding yeast, E. coli, etc., with a total of 1600 pathways
- Time series and Two-factor data analysis: The service currently supports clustering and visualization (including interactive 3D PCA visualization and two-way heatmaps with hierarchical clustering), two-way ANOVA for univariate two-factor analysis, multivariate empirical Bayes time-series analysis (MEBA) for detecting distinctive temporal profiles across different experimental conditions, and ANOVA-simultaneous component analysis (ASCA) for identification of major patterns associated with each experimental factor (and their interactions)
- Sample size and power analysis: Users can upload a dataset either from a pilot study or from a similar study to compute the minimum number of samples required to detect the effect within a certain degree of confidence, as well as to estimate the power of the current study design.
- Biomarker analysis: The service provides receiver operating characteristic (ROC) curve based approach for evaluating the performance of potential biomarkers. It offers classical univariate ROC analysis as well as more modern multivariate ROC curve analysis based on PLS-DA, SVM or Random Forests.
- Integrated pathway analysis: The service allows users to simultaneously analyze genes and metabolites of interest within the context of metabolic pathways. Only data from human, mouse and rat are supported currently.
- Other Utilities: Compound ID conversion, batch effect correction and lipidomics data analysis are available.
References: Xia, J., Sinelnikov, I., Han, B., and Wishart, D.S. (2015) MetaboAnalyst 3.0 - making metabolomics more meaningful. Nucl. Acids Res. 43, W251-257.
See also MetaboAnalyst Tutorials