MetaboAnalyst 5.0

Functional and statistical analysis of metabolomics data

Description

MetaboAnalyst 5.0 is a user-friendly web-based tool for statistical and functional interpretation of metabolomics (including lipidomics) data. The tool supports a wide-variety of univariate or multivariate statistical and machine learning methods to identify important features and patterns. MetaboAnalyst 5.0 also has methods for raw spectra processing, biomarker identification and meta-analysis. The tool supports Pathway Analysis for 26 organisms as well as Enrichment analysis of ~ 9, 000 metabolite sets, including all lipid classes from LipidMaps. Herein, the tool implements a smart-matching algorithm to aid users match their named-lipids with the internal MetaboAnalyst compound database. The tool provides rich and interactive visualizations to support users to explore their data. Finally, all tabular outputs, publication-quality figures, and PDF summary report are available at the end of a user’s session for download.

Technical Information

License:

GPL (Academic) / Commercial license (Commercial use)

GUI:

Yes

CLI:

Desktop client:

Yes

Web platform:

Yes

Input formats:

Compound list,

Peak list,

mzML,

TXT,

CSV

Output formats:

PDF,

PNG,

CSV

Platforms:

MacOS,

Linux,

Windows

Programming languages:

JavaScript,

Java,

Source code repository:

Download / Web-service link:

Yes

Documentation and user guide:

Yes

Tasks

6) Analysis and visualization of lipidomics data

Data pre-treatments:

Scaling (mean centring, auto, pareto, range),

Normalization (Sample-specific, Sum, median, PQN, Pooled groups, Reference feature),

Filtering

Missing data handling:

R-based methods

Univariate statistical analysis methods:

Correlation networks,

Pattern search,

Correlation heatmaps,

ANOVA,

Fold-change analysis,

T-test

Unsupervised multivariate statistical analysis methods:

PCA

Supervised multivariate statistical analysis methods:

OrthoPLS-DA,

sPLS-DA,

PLS-DA

Feature identification:

Significance analysis of microarray (SAM), Empirical Bayesian Analysis of Microarray (EBAM)

Clustering and correlation analysis methods:

Partitional (K-means, SOM) clustering,

Hierarchical (dendrogram, heatmap)

Classification and feature selection:

Support Vector Machine,

Random Forest

Direct connection to identification results:

Yes