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Genome Canada Genome Quebec NSERC TMIC
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  • Enhanced support for Feature Selection for large data in Biomarker Tester (08/12/2017);
  • The downloaded correlation matrix is now synchronized with current clustering (08/07/2017);
  • Fixed the bug for computing ASCA permutation (07/27/2017);
  • More than 1.6 million jobs have been processed over the last 12 months (06/08/2017);
  • Added support for correlation analysis on either samples or features (06/07/2017);
  • Added support for generating heatmap on group averages (06/05/2017);
  • Check out our MicrobiomeAnalyst tool for comprehensive analysis of microbiome data (04/28/2017);
  • Fixed issues with PDF report generation (03/15/2017);
  • Added support for peak filtering based on QC samples for untargeted metabolomics (03/10/2017);
  • Added support for "flipping" PCA for cross-study comparison (02/09/2017);
  • Added support for network summary of enrichment analysis result (02/06/2017);
  • Fixed the bug in feature table display in Biomarker Tester module (01/05/2017);
Read more ......

Please Cite:

Xia, J. and Wishart, D.S. (2016) Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis Current Protocols in Bioinformatics, 55:14.10.1-14.10.91.

Xia, J., Sinelnikov, I., Han, B., and Wishart, D.S. (2015) MetaboAnalyst 3.0 - making metabolomics more meaningful . Nucl. Acids Res. 43, W251-257.

Xia, J., Mandal, R., Sinelnikov, I., Broadhurst, D., and Wishart, D.S. (2012) MetaboAnalyst 2.0 - a comprehensive server for metabolomic data analysis . Nucl. Acids Res. 40, W127-133.

Xia, J. and Wishart, D.S. (2011) Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst Nature Protocols 6 (6), 743-760.

Xia, J. and Wishart, D.S. (2011) Metabolomic data processing, analysis, and interpretation using MetaboAnalyst Current Protocols in Bioinformatics, 14.10.1-14.10.48.

Xia, J., Psychogios, N., Young, N. and Wishart, D.S. (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucl. Acids Res. 37, W652-660.

Xia, J., Broadhurst, D., Wilson, M. and Wishart, D. (2013) Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics, 9, 280-299. (biomarker analysis)

Xia, J., Sinelnikov, I., and Wishart, D.S. (2011) MetATT - a web-based metabolomic tool for analyzing time-series and two-factor data sets. Bioinformatics, 27, 2455-2456 (time-series and two-factor analysis)

Xia, J. and Wishart, D.S. (2010) MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics, 26, 2342-2344 (metabolic pathway analysis)

Xia, J. and Wishart, D.S. (2010) MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Research, 38, W71-77 (metabolite set enrichment analysis)

Project objective: To provide a user-friendly, web-based analytical pipeline for high-throughput metabolomics studies. In particular, MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies.

Data formats: Diverse data types from current metabolomic studies are supported (details) including compound concentrations, NMR/MS spectral bins, NMR/MS peak intensity table, NMR/MS peak lists, and LC/GC-MS spectra.

Data processing: Depending on the type of the uploaded data, different data processing options are available (details). This is followed by data normalization steps including normalization by constant sum, normalization by a reference sample/feature, sample specific normalization, auto/Pareto/range scaling, etc.

Statistical analysis: A wide array of commonly used statistical and machine learning methods are available: univariate - fold change analysis, t-tests,volcano plot, and one-way ANOVA, correlation analysis; multivariate - principal component analysis (PCA), partial least squares - discriminant analysis (PLS-DA) and orthogonal partial least squares - discriminant analysis (Orthogonal PLS-DA); high-dimensional feature selection - significance analysis of microarrays (and metabolites) (SAM) and empirical Bayesian analysis of microarrays (and metabolites) (EBAM); clustering - dendrogram, heatmap, K-means, and self organizing map (SOM)); supervised classification - random forests and support vector machine (SVM).

Functional enrichment analysis: The service performs metatolite set enrichment analysis (MSEA) for human and mammalian species. It can accept a list of compound names, a list of compound names with concentrations, or a concentration table. The analysis is based on several libraries containing ~6300 groups of biologically meaningful metabolite sets collected primarily from human studies;

Metabolic pathway analysis: The service currently supports pathway analysis (including pathway enrichment analysis and pathway topology analysis) and visualization for 21 model organisms, including Human, Mouse, Rat, Cow, Chicken, Zebrafish, Arabidopsis thaliana, Rice, Drosophila, Malaria, Budding yeast, E.coli., etc., with a total of 1600 pathways;

Time series and Two-factor data analysis: The service currently supports clustering and visualization (including interactive 3D PCA visualization and two-way heatmaps with hierarchical clustering), two-way ANOVA for univariate two-factor analysis, multivariate empirical Bayes time-series analysis (MEBA) for detecting distinctive temporal profiles across different experimental conditions, and ANOVA-simultaneous component analysis (ASCA) for identification of major patterns associated with each experimental factor (and their interactions);

Biomarker analysis: The service provides receiver operating characteristic (ROC) curve based approach for evaluating the performance of potential biomarkers. It offers classical univariate ROC analysis as well as more modern multivariate ROC curve analysis based on PLS-DA, SVM or Random Forests. In addition, users can manually pick biomarkers or to set up hold-out samples for flexible evaluation and validation;

Sample size and power analysis: Users can upload a dataset either from a pilot study or from a similar study to compute the minimum number of samples required to detect the effect within a certain degree of confidence, as well as to estimate the power of the current study design.

Integrated pathway analysis: The service allows users to simultaneously analyze genes and metabolites of interest within the context of metabolic pathways. Only data from human, mouse and rat are supported currently.

Image generation: Important images can be re-produced in high resolution in various format such as .png, .tiff, .ps, etc for publication purposes

Report generation: Upon completion, a comprehensive PDF report will be generated documenting each step performed along with corresponding tabular and graphical results. The processed data and images are also available for download.