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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 21 model organisms, including Human, Mouse, Rat, Cow, Chicken, Zebrafish, Arabidopsis thaliana, Rice, Drosophila, Malaria, S. cerevisae, E.coli, and others species.
MS Peaks to Pathway Activities
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 21 organisms including Human, Mouse, Zebrafish, C. elegans, and other species.
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 ~6300 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 data generated from Human, Mouse and Rat.
Network Explorer
This module allows users to upload list(s) of metabolites (metabolomics), genes (transcriptomics) or KEGG orthologs (metagenomics), and then visually explore their relationships within the context of five different biological networks curated based on their known associations.
Biomarker Meta-analysis
This module provides statistical methods to identify robust biomarkers through meta-analysis of multiple independent metabolomics data sets 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.
Spectral Analysis
Provides links to several web-based tools - Bayesil for compound identification and quantification using 1D 1H NMR spectra; GC-AutoFit for automated compound identification and quantification for GC-MS; and XCMSonline for LC-MS spectral processing and analysis.
Other Utilities
The Batch effect correction corrects for batch effect without using QC samples. The compound ID conversion performs batch conversion between compound names and various database IDs; The Lipidomics analysis computes the most probable and uplimit concentrations from lipidomics data.
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Spectra Analysis and Annotation
Raw spectra processing and annotation can be performed using the web-based tools listed below. The resulting feature (metabolites or peaks) abundance table can be uploaded to MetaboAnalyst for further analysis.
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