README.md
In xia-lab/MetaboAnalystR: An R Package for Comprehensive Analysis of Metabolomics Data
MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics
Description
MetaboAnalystR 4.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including metabolomic data analysis, visualization, and functional interpretation. The package is synchronized with the MetaboAnalyst web server. After installing and loading the package, users will be able to reproduce the same results from their local computers using the corresponding R command history downloaded from MetaboAnalyst web site, thereby achieving maximum flexibility and reproducibility.
The version 4.0 aims to address three key challenges facing global metabolomics. By leveraging the best practices established by the community, MetaboAnalyst R 4.0 offers three key features:
1) an auto-optimized feature detection and quantification module for LC-MS1 spectra processing;
2) a streamlined MS/MS spectra deconvolution and compound annotation module for both data-dependent acquisition (DDA) or data-independent acquisition (DIA);
3) a sensitive and debiased functional interpretation module for functional analysis directly from LC-MS and MS/MS results.
MetaboAnalystR 4.0 comes with a large collection of knowledgebases (~500,000 entries of metabolite sets) and spectra databases (~1.5 million MS2 spectra) to support local large-scale processing or using our API service.
Our comprehensive benchmark studies show that MetaboAnalystR 4.0 can significantly improve the quantification accuracy and identification coverage of the metabolome. Serial dilutions demonstrate that MetaboAnalystR 4.0 can accurately detect and identify > 10% more high-quality MS and MS/MS features. For both DDA and DIA datasets, MetaboAnalystR 4.0 can increase the true positive rate of chemical identification by > 40% without increasing false identifications. The increased coverage and accuracy enable more accurate biological insights. In conclusion, MetaboAnalystR 4.0 provides an efficient pipeline that bridges LC-MS/MS data processing to biological insights in the open-source R environment.
Getting Started
Step 1. Install package dependencies
To use MetaboAnalystR 4.0, first install all package dependencies. Ensure that you have necessary system environment configured.
For Linux (e.g. Ubuntu 18.04/20.04): libcairo2-dev, libnetcdf-dev, libxml2, libxt-dev and libssl-dev should be installed at frist;
For Windows (e.g. 7/8/8.1/10): Rtools should be installed.
For Mac OS: In order to compile R for Mac OS, you need Xcode and GNU Fortran compiler installed. We suggest you follow these steps to help with your installation.
R base with version > 3.6.1 is recommended. The compatibility of latest version (v4.0.0) is under evaluation. As for installation of package dependencies, there are two options:
Option 1
Enter the R function (metanr_packages) and then use the function. A printed message will appear informing you whether or not any R packages were installed.
Function to download packages:
metanr_packages <- function(){
metr_pkgs <- c("impute", "pcaMethods", "globaltest", "GlobalAncova", "Rgraphviz", "preprocessCore", "genefilter", "sva", "limma", "KEGGgraph", "siggenes","BiocParallel", "MSnbase", "multtest","RBGL","edgeR","fgsea","devtools","crmn","httr","qs")
list_installed <- installed.packages()
new_pkgs <- subset(metr_pkgs, !(metr_pkgs %in% list_installed[, "Package"]))
if(length(new_pkgs)!=0){
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(new_pkgs)
print(c(new_pkgs, " packages added..."))
}
if((length(new_pkgs)<1)){
print("No new packages added...")
}
}
Usage of function:
metanr_packages()
Option 2
Use the pacman R package (for those with >R 3.5.1).
install.packages("pacman")
library(pacman)
pacman::p_load(c("impute", "pcaMethods", "globaltest", "GlobalAncova", "Rgraphviz", "preprocessCore", "genefilter", "sva", "limma", "KEGGgraph", "siggenes","BiocParallel", "MSnbase", "multtest","RBGL","edgeR","fgsea","httr","qs"))
Step 2. Install the package
MetaboAnalystR 4.0 is freely available from GitHub. The package documentation, including the vignettes for each module and user manual is available within the downloaded R package file. You can install the MetaboAnalylstR 3.0 via any of the three options: A) using the R package devtools, B) cloning the github, C) manually downloading the .tar.gz file. Note that the MetaboAnalystR 3.0 github will have the most up-to-date version of the package.
Option A) Install the package directly from github using the devtools package. Open R and enter:
Due to issues with Latex, some users may find that they are only able to install MetaboAnalystR 4.0 without any documentation (i.e. vignettes).
# Step 1: Install devtools
install.packages("devtools")
library(devtools)
# Step 2: Install MetaboAnalystR with documentation
devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = TRUE, build_manual =T)
# Step 2: Install MetaboAnalystR without documentation
devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = FALSE)
Option B) Clone Github and install locally
The * must be replaced by what is actually downloaded and built.
git clone https://github.com/xia-lab/MetaboAnalystR.git
R CMD build MetaboAnalystR
R CMD INSTALL MetaboAnalystR_3.0.3.tar.gz
Option C) Manual download of MetaboAnalystR_3.0.3.tar.gz and install locally
Manually download the .tar.gz file from here.
cd ~/Downloads
R CMD INSTALL MetaboAnalystR_3.0.3.tar.gz
Case Studies
MetaboAnalystR 3.0: Towards an Optimized Workflow for Global Metabolomics
The case studies have been preformed in our article of this version here (available online now). The example running R code of this article have been provided as a vignette inside the R package.
MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
The R scripts to perform all of the analysis from our previous manuscript "MetaboAnalystR 2.0: From Raw Spectra to Biological Insights" can be found here.
The detailed tutorial of the outdated version to perform a comprehensive end-to-end metabolomics data workflow from raw data preprocessing to knowledge-based analysis still works. The tutorial is available as a PDF is also available inside the R package as a vignette.
MetaboAnalystR 1.0: flexible and reproducible analysis of metabolomics data
To demonstrate the functionality, flexibility, and scalability of the MetaboAnalystR v1.0.0 package, three use-cases using two sets of metabolomics data is available here. In this folder you will find detailed discussions and comparisons with the MetaboAnalyst web-platform.
Tutorials
For detailed tutorials on how to use MetaboAnalystR 4.0, please refer to the R package vignettes. These vignettes include detailed step-by-step workflows with example data for each of the main MetaboAnalytR 4.0 modules, a case-study showcasing the new end-to-end functionality of MetaboAnalystR 4.0. The raw data processing workflow has been accelerated and gradually mature. Note, the functions below work only if the R package vignettes were built.
Within R:
vignette(package="MetaboAnalystR")
Within a web-browser:
browseVignettes("MetaboAnalystR")
Citation
MetaboAnalystR package has been developed by the XiaLab at McGill University. We encourage users to further develop the package to suit their needs. If you use the R package, please cite us:
-
Zhiqiang Pang, Jasmine Chong, Shuzhao Li and Jianguo Xia. "MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics" link
-
Jasmine Chong, Mai Yamamoto, and Jianguo Xia. "MetaboAnalystR 2.0: From Raw Spectra to Biological Insights."
Metabolites 9.3 (2019): 57. link
-
Jasmine Chong, and Jianguo Xia. "MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data." Bioinformatics 34.24 (2018): 4313-4314. link
From within R:
citation("MetaboAnalystR")
Previous Version Releases
MetaboAnalystR 3.0.2 can be downloaded here.
MetaboAnalystR 3.0.1 can be downloaded here.
MetaboAnalystR 3.0.0 can be downloaded here.
MetaboAnalystR 2.0.4 can be downloaded here.
Bugs or feature requests
To inform us of any bugs or requests, please open a new issue (and @ Zhiqiang-PANG !!) or send an email to zhiqiang.pang@mail.mcgill.ca.
MetaboAnalystR History & Updates
05-30-2023 - Version Update: 4.0.0: pre-release of version 4.0
11-27-2022 - Version Update: 3.3.0: pre-version of 4.0 by fixing bugs and stablize the functions
10-30-2022 - Bug fixes and update
02-01-2022 - Raw data processing pipeline update
07-18-2021 - Bug fixed and API services fixes
06-18-2020 - Version Update: 3.2.0
01-20-2021 - Sync R code, updates bug fixes
12-30-2020 - Version Update: 3.1.0
09-22-2020 - Sync R code w. web, change .rds files to .qs (requires .qs R package).
07-23-2020 - added new function for dot plots for enrichment analysis (PlotEnrichDotPlot).
06-27-2020 - Version Update: 3.0.3 - update bug fixes for raw data processing, and added functions for Empty MS scan removal and MS data centroid;
05-08-2020 - Version Update: 3.0.2 - update bug fixes for raw data processing plot, and slim down the database to speed up the installation;
05-01-2020 - Version Update: 3.0.1 - update bug fixes for batch effect correction and compatible with web server, and added Signal Drift Correction Method;
04-10-2020 - Version Update: 3.0.0 - ultra-fast parameter optimization for peak picking, automated batch effect correction, and improved pathway activity prediction from LC-MS peaks;
03-12-2020 - Version Update: 2.0.4 - added retention time for MS Peaks to Paths (Beta)! + added troubleshooting for compound mapping
02-19-2020 - Version Update: 2.0.3 - update bug fixes w. web server
01-14-2020 - Version Update: 2.0.2 - updated gene mapping to use internal SQlite database (included in /inst), added Rserve engine (Rcode synchronized with web-server) + added functions for users to create their own mummichog libraries
01-08-2020 - Updating code for box plots + mummichog
04-11-2019 - Version Update: 2.0.1 - updated underlying code of MS Peaks to Paths, new functions to create plots to summarize results of mummichog and GSEA analysis, fixed bugs with compound name matching, updated documentation
03-05-2019 - Version Update: 2.0.0! - added function for graphical integration of results from mummichog and fGSEA, added new tutorial with example data from the fecal metabolome of IBD patients
03-03-2019 - Version Update: 1.0.4 - added support for pathway activity prediction using fGSEA; major release coming soon after bug fixes
02-11-2019 - Version Update: 1.0.3 - updated underlying R code w. changes to MetaboAnalyst 4.53 + updated documentation
11-20-2018 - Version Update: 1.0.2 - updated links in R code (https) + underlying code w. changes to MetaboAnalyst 4.39
05-25-2018 - Version Update: 1.0.1 - updated underlying R code w. changes to MetaboAnalyst 4.09
04-20-2018 - Submission to CRAN
04-16-2018 - Testing with R Version 3.4.4
04-10-2018 - Updated underlying R code w. changes to MetaboAnalyst 4.0
03-23-2018 - Added 2 more package dependencies
02-23-2018 - Minor bug fixes based on user feedback (MetaboAnalystR_1.0.0.6.tar.gz)
02-05-2018 - Update MetaboAnalystR with 3 new modules in conjunction with the release of MetaboAnalyst Version 4
xia-lab/MetaboAnalystR documentation built on April 20, 2024, 8:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics
Description
MetaboAnalystR 4.0 contains the R functions and libraries underlying the popular MetaboAnalyst web server, including metabolomic data analysis, visualization, and functional interpretation. The package is synchronized with the MetaboAnalyst web server. After installing and loading the package, users will be able to reproduce the same results from their local computers using the corresponding R command history downloaded from MetaboAnalyst web site, thereby achieving maximum flexibility and reproducibility.
The version 4.0 aims to address three key challenges facing global metabolomics. By leveraging the best practices established by the community, MetaboAnalyst R 4.0 offers three key features: 1) an auto-optimized feature detection and quantification module for LC-MS1 spectra processing; 2) a streamlined MS/MS spectra deconvolution and compound annotation module for both data-dependent acquisition (DDA) or data-independent acquisition (DIA); 3) a sensitive and debiased functional interpretation module for functional analysis directly from LC-MS and MS/MS results.
MetaboAnalystR 4.0 comes with a large collection of knowledgebases (~500,000 entries of metabolite sets) and spectra databases (~1.5 million MS2 spectra) to support local large-scale processing or using our API service.
Our comprehensive benchmark studies show that MetaboAnalystR 4.0 can significantly improve the quantification accuracy and identification coverage of the metabolome. Serial dilutions demonstrate that MetaboAnalystR 4.0 can accurately detect and identify > 10% more high-quality MS and MS/MS features. For both DDA and DIA datasets, MetaboAnalystR 4.0 can increase the true positive rate of chemical identification by > 40% without increasing false identifications. The increased coverage and accuracy enable more accurate biological insights. In conclusion, MetaboAnalystR 4.0 provides an efficient pipeline that bridges LC-MS/MS data processing to biological insights in the open-source R environment.
Getting Started
Step 1. Install package dependencies
To use MetaboAnalystR 4.0, first install all package dependencies. Ensure that you have necessary system environment configured.
For Linux (e.g. Ubuntu 18.04/20.04): libcairo2-dev, libnetcdf-dev, libxml2, libxt-dev and libssl-dev should be installed at frist;
For Windows (e.g. 7/8/8.1/10): Rtools should be installed.
For Mac OS: In order to compile R for Mac OS, you need Xcode and GNU Fortran compiler installed. We suggest you follow these steps to help with your installation.
R base with version > 3.6.1 is recommended. The compatibility of latest version (v4.0.0) is under evaluation. As for installation of package dependencies, there are two options:
Option 1
Enter the R function (metanr_packages) and then use the function. A printed message will appear informing you whether or not any R packages were installed.
Function to download packages:
metanr_packages <- function(){
metr_pkgs <- c("impute", "pcaMethods", "globaltest", "GlobalAncova", "Rgraphviz", "preprocessCore", "genefilter", "sva", "limma", "KEGGgraph", "siggenes","BiocParallel", "MSnbase", "multtest","RBGL","edgeR","fgsea","devtools","crmn","httr","qs")
list_installed <- installed.packages()
new_pkgs <- subset(metr_pkgs, !(metr_pkgs %in% list_installed[, "Package"]))
if(length(new_pkgs)!=0){
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(new_pkgs)
print(c(new_pkgs, " packages added..."))
}
if((length(new_pkgs)<1)){
print("No new packages added...")
}
}
Usage of function:
metanr_packages()
Option 2
Use the pacman R package (for those with >R 3.5.1).
install.packages("pacman")
library(pacman)
pacman::p_load(c("impute", "pcaMethods", "globaltest", "GlobalAncova", "Rgraphviz", "preprocessCore", "genefilter", "sva", "limma", "KEGGgraph", "siggenes","BiocParallel", "MSnbase", "multtest","RBGL","edgeR","fgsea","httr","qs"))
Step 2. Install the package
MetaboAnalystR 4.0 is freely available from GitHub. The package documentation, including the vignettes for each module and user manual is available within the downloaded R package file. You can install the MetaboAnalylstR 3.0 via any of the three options: A) using the R package devtools, B) cloning the github, C) manually downloading the .tar.gz file. Note that the MetaboAnalystR 3.0 github will have the most up-to-date version of the package.
Option A) Install the package directly from github using the devtools package. Open R and enter:
Due to issues with Latex, some users may find that they are only able to install MetaboAnalystR 4.0 without any documentation (i.e. vignettes).
# Step 1: Install devtools
install.packages("devtools")
library(devtools)
# Step 2: Install MetaboAnalystR with documentation
devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = TRUE, build_manual =T)
# Step 2: Install MetaboAnalystR without documentation
devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = FALSE)
Option B) Clone Github and install locally
The * must be replaced by what is actually downloaded and built.
git clone https://github.com/xia-lab/MetaboAnalystR.git
R CMD build MetaboAnalystR
R CMD INSTALL MetaboAnalystR_3.0.3.tar.gz
Option C) Manual download of MetaboAnalystR_3.0.3.tar.gz and install locally
Manually download the .tar.gz file from here.
cd ~/Downloads
R CMD INSTALL MetaboAnalystR_3.0.3.tar.gz
Case Studies
MetaboAnalystR 3.0: Towards an Optimized Workflow for Global Metabolomics
The case studies have been preformed in our article of this version here (available online now). The example running R code of this article have been provided as a vignette inside the R package.
MetaboAnalystR 2.0: From Raw Spectra to Biological Insights
The R scripts to perform all of the analysis from our previous manuscript "MetaboAnalystR 2.0: From Raw Spectra to Biological Insights" can be found here.
The detailed tutorial of the outdated version to perform a comprehensive end-to-end metabolomics data workflow from raw data preprocessing to knowledge-based analysis still works. The tutorial is available as a PDF is also available inside the R package as a vignette.
MetaboAnalystR 1.0: flexible and reproducible analysis of metabolomics data
To demonstrate the functionality, flexibility, and scalability of the MetaboAnalystR v1.0.0 package, three use-cases using two sets of metabolomics data is available here. In this folder you will find detailed discussions and comparisons with the MetaboAnalyst web-platform.
Tutorials
For detailed tutorials on how to use MetaboAnalystR 4.0, please refer to the R package vignettes. These vignettes include detailed step-by-step workflows with example data for each of the main MetaboAnalytR 4.0 modules, a case-study showcasing the new end-to-end functionality of MetaboAnalystR 4.0. The raw data processing workflow has been accelerated and gradually mature. Note, the functions below work only if the R package vignettes were built.
Within R:
vignette(package="MetaboAnalystR")
Within a web-browser:
browseVignettes("MetaboAnalystR")
Citation
MetaboAnalystR package has been developed by the XiaLab at McGill University. We encourage users to further develop the package to suit their needs. If you use the R package, please cite us:
-
Zhiqiang Pang, Jasmine Chong, Shuzhao Li and Jianguo Xia. "MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics" link
-
Jasmine Chong, Mai Yamamoto, and Jianguo Xia. "MetaboAnalystR 2.0: From Raw Spectra to Biological Insights." Metabolites 9.3 (2019): 57. link
-
Jasmine Chong, and Jianguo Xia. "MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data." Bioinformatics 34.24 (2018): 4313-4314. link
From within R:
citation("MetaboAnalystR")
Previous Version Releases
MetaboAnalystR 3.0.2 can be downloaded here. MetaboAnalystR 3.0.1 can be downloaded here. MetaboAnalystR 3.0.0 can be downloaded here. MetaboAnalystR 2.0.4 can be downloaded here.
Bugs or feature requests
To inform us of any bugs or requests, please open a new issue (and @ Zhiqiang-PANG !!) or send an email to zhiqiang.pang@mail.mcgill.ca.
MetaboAnalystR History & Updates
05-30-2023 - Version Update: 4.0.0: pre-release of version 4.0
11-27-2022 - Version Update: 3.3.0: pre-version of 4.0 by fixing bugs and stablize the functions
10-30-2022 - Bug fixes and update
02-01-2022 - Raw data processing pipeline update
07-18-2021 - Bug fixed and API services fixes
06-18-2020 - Version Update: 3.2.0
01-20-2021 - Sync R code, updates bug fixes
12-30-2020 - Version Update: 3.1.0
09-22-2020 - Sync R code w. web, change .rds files to .qs (requires .qs R package).
07-23-2020 - added new function for dot plots for enrichment analysis (PlotEnrichDotPlot).
06-27-2020 - Version Update: 3.0.3 - update bug fixes for raw data processing, and added functions for Empty MS scan removal and MS data centroid;
05-08-2020 - Version Update: 3.0.2 - update bug fixes for raw data processing plot, and slim down the database to speed up the installation;
05-01-2020 - Version Update: 3.0.1 - update bug fixes for batch effect correction and compatible with web server, and added Signal Drift Correction Method;
04-10-2020 - Version Update: 3.0.0 - ultra-fast parameter optimization for peak picking, automated batch effect correction, and improved pathway activity prediction from LC-MS peaks;
03-12-2020 - Version Update: 2.0.4 - added retention time for MS Peaks to Paths (Beta)! + added troubleshooting for compound mapping
02-19-2020 - Version Update: 2.0.3 - update bug fixes w. web server
01-14-2020 - Version Update: 2.0.2 - updated gene mapping to use internal SQlite database (included in /inst), added Rserve engine (Rcode synchronized with web-server) + added functions for users to create their own mummichog libraries
01-08-2020 - Updating code for box plots + mummichog
04-11-2019 - Version Update: 2.0.1 - updated underlying code of MS Peaks to Paths, new functions to create plots to summarize results of mummichog and GSEA analysis, fixed bugs with compound name matching, updated documentation
03-05-2019 - Version Update: 2.0.0! - added function for graphical integration of results from mummichog and fGSEA, added new tutorial with example data from the fecal metabolome of IBD patients
03-03-2019 - Version Update: 1.0.4 - added support for pathway activity prediction using fGSEA; major release coming soon after bug fixes
02-11-2019 - Version Update: 1.0.3 - updated underlying R code w. changes to MetaboAnalyst 4.53 + updated documentation
11-20-2018 - Version Update: 1.0.2 - updated links in R code (https) + underlying code w. changes to MetaboAnalyst 4.39
05-25-2018 - Version Update: 1.0.1 - updated underlying R code w. changes to MetaboAnalyst 4.09
04-20-2018 - Submission to CRAN
04-16-2018 - Testing with R Version 3.4.4
04-10-2018 - Updated underlying R code w. changes to MetaboAnalyst 4.0
03-23-2018 - Added 2 more package dependencies
02-23-2018 - Minor bug fixes based on user feedback (MetaboAnalystR_1.0.0.6.tar.gz)
02-05-2018 - Update MetaboAnalystR with 3 new modules in conjunction with the release of MetaboAnalyst Version 4
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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