xcmsrocker

Rocker image for metabolomics data analysis

For Java, you could select MSDK.

For C/C++, you could select openms or ProteoWizard.

For C#, you could select Prime.

For Matlab, you could select Bioinformatics Toolbox.

For python, you could select emzed.

However, I suggest this R-based image for starter and hopefully we could see a Julia platform in the future.

Same software is not the end and detailed workflow would be the key to reproducible research in metabolomics studies. Xcmsrocker is a linux based docker image to simply the usage of R based metabolomics software. It includes multiple mainstream R packages used in metabolomics study with RStduio as IDE. Such image could be easy deployed on single machine or clusters on the go. To make the users do the right thing for metabolomics studies, rmwf package is attached in this image to provide detailed workflow template( File - New File - R Markdown - From Template - Select template with {rmwf}) from different research groups and facilitate the users to make metabolomics data analysis and/or comparison on the gaints' shoulder.

Here is a nice review on R package for metabolomics.

patRoon is a similar project.

MetaboAnalystR R functions for MetaboAnalyst and they maintain docker image officially.

Usage

1. Install Docker

2. Pull the Rocker image docker pull yufree/xcmsrocker:latest

3. Use docker run -e PASSWORD=xcmsrocker -p 8787:8787 yufree/xcmsrocker to start the image

4. Open the browser and visit http://localhost:8787 or http://[your-ip-address]:8787 to power on RStudio server

5. Default user name is rstudio and password is xcmsrocker

6. Enjoy your data analysis!

Packages

Peak picking

• xcms Generate peaks list/EIC/diffreport
• apLCMS Generate peaks list
• x13cms global tracking of isotopic labels in untargeted metabolomics

Improved Peak picking

• IPO For xcms peak picking optimazation
• Autotuner Automated parameter selection for untargeted metabolomics data processing
• warpgroup increase the precision for xcms peak picking and retention time correction
• xMSanalyzer improved Peak picking for xcms and apLCMS

For MS/MS

• decoMS2 An Untargeted Metabolomic Workflow to Improve Structural Characterization of Metabolites
• msPurity Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics
• MetDIA Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition for SWATH

Peak filter/visulization/workflow

• enviGCMS Filter peaks based on experimental design
• metaMS An open-source pipeline for GC–MS-based untargeted metabolomics
• ChemoSpec Exploratory Chemometrics for Spectroscopy

Peak annotation/group/selection

• pmd Select the independent peaks based on paired mass distance analysis
• CAMERA Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments
• RAMClustR A feature clustering algorithm for non-targeted mass spectrometric metabolomics data
• compMS2Miner comprehensive and automatable annotation of metabolomic high-resolution LC-MS datasets
• mz.unity Defining and Detecting Complex Peak Relationships in Mass Spectral Data
• Rdisop Decomposition of Isotopic Patterns
• InterpretMSSpectrum Annotate and interpret deconvoluted mass spectra (mass*intensity pairs) from high resolution mass spectrometry devices
• nontarget Detecting Isotope, Adduct and Homologue Relations in LC-MS Data
• classyfireR Retrieve existing entity classifications from SMILES or InChls.

Batch correction

• Msprep Summarization, normalization and diagnostics for processing of mass spectrometry–based metabolomic data by Median, Quantile, Cross-Contribution Compensating Multiple Standard Normalization (CRMN), Surrogate Variable Analysis (SVA) and Removal of Unwanted Variation (RUV).
• BatchCorrMetabolomics Improved batch correction in untargeted MS-based metabolomics by pool QC.

Peaks identification

• xMSannotator MS1 annotation
• MetFragr The R package enables functionalities from the MetFrag Commandline tool to be used within the R programming language.

Omics

• xMWAS a data-driven integration and differential network analysis tool.
• MetabNet An R Package for Metabolic Association Analysis of High-Resolution Metabolomics Data.

Statistical analysis

• MetMSLine R functions for automation of biomarker discovery based on processing downstream of peak picking softwares
• caret general machine learning workflow for more than 200 models
• caretEnsemble Functions for creating ensembles of caret models
• pROC Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
• gWQS Fits Weighted Quantile Sum (WQS) regressions for continuous, binomial, multinomial and count outcomes.

Chemometrics

• rcdk Interface to the 'CDK' Libraries
• ChemmineR Cheminformatics Toolkit for R
• webchem Chemical Information from the Web

Reproducible research

• Risa Converting experimental metadata from ISA-tab into Bioconductor data structures
• rmwf Reproducilble Metabolomics WorkFlow(RMWF) is a R package for xcmsrocker. It will show the workflow templates and demo data for different R-based metabolomics software.

Links

• Use docker to package your metabolomics study
• Docker Hub
• Docker tutorial
• Rocker
• Report extra metabolomics packages or issues
• Meta-Workflow