Module Overview

About the modules

Short descriptions of each module. Click a title to start, or use the grid above.
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.
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).
Spatial Compound Table
This module analyzes spatial tables of named metabolites or lipids (targeted assays or annotated exports) with spot coordinates and regions. After name mapping, normalization and Moran's I quality control, users explore tissue maps, pseudobulk differential analysis between regions, compound-set analysis and set enrichment.
Spatial MS Peaks
This module analyzes spatial tables of m/z features (MALDI/DESI pixels, Visium-linked MSI, SCiLS Lab or Cardinal exports) with spot coordinates and regions. After normalization and Moran's I quality control, users explore tissue maps, pseudobulk differential analysis, pathway-guided annotation, compound sets and microbial taxon-specific markers.
Spatial Data Bridge
This module brings the exports of other spatial tools (SCiLS Lab, METASPACE, 10x Visium / Space Ranger) into the tables of the spatial modules, derives regions of interest by clustering, merges several sections into one data set, and pairs a section with LC-MS metabolomics of the same tissue.
Spatial Multi-Omics
This module integrates spatial metabolomics with spatial transcriptomics (or proteomics) measured on the same section: metabolite pixels are mapped onto the spots of the second omic, both layers are normalized, and users explore joint clustering, cross-omic co-localisation, pseudobulk comparison between regions and joint pathway analysis.
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.
Enrichment Analysis
This module performs metabolite set enrichment analysis (MSEA) on a list of compound names, a list with concentrations, or a concentration table, against 15 libraries containing ~13,000 biologically meaningful metabolite sets (pathways, disease signatures, locations, chemical classes) collected primarily from human studies.
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 Analysis
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.
Study Design & Power
This module plans the sample size of a study: a priori power for a planned design from expected effect sizes, or, from pilot data, power by the effect-size model (SSPA), by resampling for the exact test to be run, as a biomarker learning curve, or per pathway. All tasks feed one power table and a methods paragraph.
Lipidomics Profiling
This module takes a table of lipid species with sample groups, parses and normalises the lipid names (rgoslin plus an internal parser), and profiles the lipidome: class composition and change, chain length x double bonds, degree of unsaturation and species statistics. The normalised tables continue in Statistical and Enrichment Analysis.
Compound ID Conversion
This is a utility module dedicated for compound name mapping using our master compound database, which contains 10X more compounds (#241,050) than the functional database (#33,908 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 Duplicate Records
This is an utility module dedicated to merging sample or feature duplicates in your metabolomics data table. These values can be merged by their simple arithmetic mean, minimum, maximum, medium, sum, or quantile. Kernel density estimation can be applied to smooth values when many duplicates (over 3) are provided.
NSERC CRC CFI TMIC Genome Canada Genome Quebec NIH