Input Data Type
Available Modules (click on a module to proceed, or scroll down to explore a total of 18 modules including utilities)
LC-MS Spectra
(mzML, mzXML or mzData)
Spectra Processing
[LC-MS w/wo MS2]
MS Peaks
(peak list or intensity table)
Peak Annotation
[MS2-DDA/DIA]
Functional Analysis
[LC-MS]
Functional Meta-analysis
[LC-MS]
Generic Format
(.csv or .txt table files)
Statistical Analysis
[one factor]
Statistical Analysis
[metadata table]
Biomarker Analysis
Statistical Meta-analysis
Dose Response Analysis
Annotated Features
(metabolite list or table)
Enrichment Analysis
Pathway Analysis
Network Analysis
Link to Genomics & Phenotypes
(metabolite list)
Causal Analysis
[Mendelian randomization]
Spectral Processing [LC-MS1 w/wo MS2]
This module allows users to upload raw LC-MS spectra (mzML, mzXML or mzData) to be processed using our optimized workflow based on MetaboAnalystR 4.0 or the latest asari algorithm. Users can also include MS2 spectra (both DDA or SWATH-DIA are supported) for peak annotation.
Peak Annotation [MS2-DIA/DDA]
This module performs MS2 peak annotation based on a comprehensive list of public databases. Users can either directly enter a two-column peak list containing m/z and intensity values (DDA); or upload a .msp file produced by MZmine or MS-DIAL after the spectral deconvolution (SWATH-DIA).
Functional Analysis [LC-MS1]
This module accepts high-resolution LC-MS spectral peak data to perform metabolic pathway enrichment analysis and visual exploration based on the mummichog or GSEA algorithms. It currently supports 26 organisms including Human, Mouse, Zebrafish, C. elegans, and other species.
Functional Meta-analysis [LC-MS1]
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.
Statistical Analysis [one factor]
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.
Statistical Analysis [metadata table]
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.
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.
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.
Dose Response Analysis
This module offers an efficient implementation of dose response analysis to quantify the relationship between the concentration of a chemical and its effects in biological samples based on their metabolomics profiles. It currently supports 10 curve fitting methods to calculate feature-level benchmark dose (BMD).
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.
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.
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.
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.
Causal Analysis [Mendelian randomization]
With growing metabolomic-genome-wide association studies (mGWAS), we can now perform causal analysis between those SNP-tagged metabolites and disease outcomes by leveraging two-sample Mendelian randomization (2SMR). Various SNP harmonization and MR diagnostics are provided.
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.
Compound ID Conversion
This is a utility module dedicated for compound name mapping using our master compound database, which contains 10X more compounds (#240,272) than the functional database (#25,344 compounds that have annotations in pathways or metabolite sets) that are used for pathway analysis or enrichment analysis.
Batch Effect Correction
This is a utility module dedicated for batch effect correction based on nine well-established methods (ComBat, EigenMS, QC-RLSC, ANCOVA, RUV-random, RUV2, RUVseq, NOMIS and CCMN). The default automated approach can return the results with least distance among batches.
Merging Technical Replicates
This is an utility module dedicated to merging the technical replicates in metabolomics studies. Replicate values can be merged by estimating with simple arithmetic mean, minimum, maximum, medium, sum, or quantile. Kernel density estimation can be applied to smooth values when many replicates are provided.
NSERC CRC CFI TMIC Genome Canada Genome Quebec NIH