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 data based on random forests and SVM.

This module aims to detect associations between phenotypes and metabolomics features with considerations of other experimental factors / covariates based on general linear models coupled with PCA and heatmaps for visualization. More options are available for two-factors / time-series data.

This module performs various biomarker analyses based on receiver operating characteristic (ROC) curves for a single or multiple biomarkers using well-established methods. It also allows users to manually specify biomarker models and perform new sample prediction.

This module performs metabolite set enrichment analysis (MSEA) for human and mammalian species based on several libraries containing ~9000 groups of metabolite sets. Users can upload either 1) a list of compounds, 2) a list of compounds with concentrations, or 3) a concentration table.

This module supports pathway analysis (integrating enrichment analysis and pathway topology analysis) and visualization for 26 model organisms, including Human, Mouse, Rat, Cow, Chicken, Zebrafish, Arabidopsis thaliana, Rice, Drosophila, Malaria, S. cerevisae, E.coli, and others species.

This module allows users to 1) upload list(s) of metabolites, genes or KEGG orthologs, and then visually explore their relationships in different biological networks; or 2) upload a data table to perform Debiased Sparse Partial Correlation (DSPC) network analysis and visual exploration.

This module performs integrated metabolic pathway analysis on results obtained from combined metabolomics and gene expression studies conducted under the same experimental conditions. It currently supports metabolomics data generated from 25 model organisms, including the Human, Mouse and Rat.

This module accepts high-resolution LC-MS spectral peak data to perform metabolic pathway enrichment analysis and visual exploration based on the well-established mummichog algorithm. It currently supports 26 organisms including Human, Mouse, Zebrafish, C. elegans, and other species.

This module allows users to upload raw LC-MS spectra (mzML, mzXML or mzData) to be processed using our optimized workflow based on MetaboAnalystR - OptiLCMS. The module supports common LC-MS platforms. The result peak intensity table can be used for statistical and functional analysis.

This module provides statistical methods to identify consistent features (metabolites or annotated peaks) through meta-analysis of multiple feature abundance tables obtained under comparable conditions. It currently supports three meta-analysis approaches based on p-values, vote counts or direct merging.

This module aims to identify robust functional profiles across multiple global metabolomics datasets via two approaches: 1) integrating functional profiles from independent studies conducted under compatible LC-MS conditions; or 2) pooling peaks from complementary instruments within the same studies.

This module allows users to upload datasets from small pilot studies or from other similar studies to calculate the minimum number of samples required to detect a statistically significant difference between two populations, based on a user-specified degree of confidence.