MetaboAnalystR 4.2: a unified LC-MS/MS workflow for global metabolomics and exposomics

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation.

MetaboAnalystR 3.3: Pre-version of version 4.0

This is a pre-version of MetaboAnalystR 4.0

MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

MetaboAnalystR 2.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including > 500 functions for metabolomic data analysis, visualization, and functional interpretation. With version 2.0, we aim to address two important gaps left in its previous version. First, raw spectral processing - the previous version offered very limited support for raw spectra processing and peak annotation. Therefore, we have implemented comprehensive support for raw LC-MS spectral data processing including peak picking, peak alignment and peak annotations leveraging the functionality of the xcms (PMIDs: 16448051, 19040729, and 20671148; version 3.4.4) and CAMERA (PMID: 22111785; version 1.38.1) R packages. Second, we have enhanced support for functional interpretation directly from m/z peaks. In addition to an efficient implementation of the mummichog algorithm (PMID: 23861661), we have added a new method to support pathway activity prediction based on the well-established GSEA algorithm (PMID: 16199517). To demonstrate this new functionality, we provide the "MetaboAnalystR 2.0 Workflow: From Raw Spectra to Biological Insights" tutorial. In this tutorial, we perform end-to-end metabolomics data analysis on a subset of clinical IBD samples, showvasing the functionality of this package to infer biological insights directly from m/z features.

MetaboAnalystR v1.0.1

New Features

Interactive 3D Visualisation

PlotPCA3DScoreImg(), PlotPLS3DScoreImg(), PlotSPLS3DScoreImg(), and iPCA.Anal() now create interactive PCA/PLS-DA/sPLS-DA/iPCA plots using the plotly R package.

Minor Updates

• Updates to internal R code in-line with changes to the MetaboAnalyst web-platform
• Addition of unit-testing
• Addition of case-studies (functionality, flexibility, and scalability)