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Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis
Abstract
MetaboAnalyst (https://www.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time‐series and two‐factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta‐analysis Basic Protocol 11: Knowledge‐based network exploration of multi‐omics data Basic Protocol 12: MetaboAnalystR introduction
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Thomas, T., Stefanoni, D., Reisz, J. A., Nemkov, T., Bertolone, L., Francis, R. O., Hudson, K. E., Zimring, J. C., Hansen, K. C., Hod, E. A., et al. (2020) · JCI Insight · doi:10.1172/jci.insight.140327 · Cited by 586
ApoE4 Impairs Neuron-Astrocyte Coupling of Fatty Acid Metabolism
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SLC25A51 is a mammalian mitochondrial NAD(+) transporter
Luongo, T. S., Eller, J. M., Lu, M.-J., Niere, M., Raith, F., Perry, C., Bornstein, M. R., Oliphint, P., Wang, L., McReynolds, M. R., et al. (2020) · Nature · doi:10.1038/s41586-020-2741-7 · Cited by 308
Dietary spermidine improves cognitive function
Schroeder, S., Hofer, S. J., Zimmermann, A., Pechlaner, R., Dammbrueck, C., Pendl, T., Marcello, G. M., Pogatschnigg, V., Bergmann, M., Müller, M., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108985 · Cited by 225
Atlas of exercise metabolism reveals time-dependent signatures of metabolic homeostasis
Sato, S., Dyar, K. A., Treebak, J. T., Jepsen, S. L., Ehrlich, A. M., Ashcroft, S. P., Trost, K., Kunzke, T., Prade, V. M., Small, L., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2021.12.016 · Cited by 203
Metadata
- Authors
- Chong, J., Wishart, D. S., & Xia, J.
- Venue
- Current Protocols in Bioinformatics
- Publisher
- Wiley
- Published
- 2019-09-20
- Volume
- 68
- Issue
- 1
- Pages
- e86-e86
- ISSN
- 1934-3396 (ISSN-L)
- 1934-340X
- OpenAlex
- W2974361058
- Cited by
- 2,166
- References
- 6
- FWCI
- 101.05
- Type
- journal article
- Language
- en
- Open access
- closed
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