Skip to content

Releases: xia-lab/MetaboAnalystR

MetaboAnalystR 3.3: Pre-version of version 4.0

27 Nov 21:44
Compare
Choose a tag to compare

This is a pre-version of MetaboAnalystR 4.0

MetaboAnalystR 2.0: From Raw Spectra to Biological Insights

06 Mar 19:35
Compare
Choose a tag to compare

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

15 Jun 18:33
Compare
Choose a tag to compare

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)