Input Data Type
Available Modules (click on a module to proceed, or scroll down for more details)
Raw Spectra
(mzML, mzXML or mzData)
LC-MS Spectral Processing
MS Peaks
(peak list or intensity table)
Functional Analysis
Functional Meta-analysis
Annotated Features
(compound list or table)
Enrichment Analysis
Pathway Analysis
Joint-Pathway Analysis
Network Analysis
Generic Format
(.csv or .txt table files)
Statistical Analysis
Biomarker Analysis
Time-series/Two-factor Analysis
Statistical Meta-analysis
Power Analysis
Other Utilities

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 data based on random forests and SVM.
Biomarker Analysis
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.
Pathway Analysis (targeted)
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.
Spectral Analysis
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.
Functional Analysis (MS Peaks)
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.
Functional Meta-analysis (MS peaks)
This module provides statistical methods to identify consistent functional changes across multiple global metabolomics datasets collected under comparable LC-MS conditions. It employs mummichog algorithm to help identify consistent functional signatures by integrating functional changes from independent studies or by pooling peaks from complementary instruments.
Time-series/Two-factor Analysis
This module supports temporal and two-factor data analysis such as two-way ANOVA, empirical Bayes time-series analysis for detecting distinctive temporal profiles, as well as ANOVA-simultaneous component analysis (ASCA) to identify major patterns associated with each experimental factor.
Enrichment Analysis
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.
Joint Pathway Analysis
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.
Network Explorer
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.
Statistical Meta-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.
Power Analysis
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.
Other Resources
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