MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data.
TL;DR: A companion R package based on the R code base of the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data.
Abstract: Summary The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data. Availability and implementation MetaboAnalystR is freely available from https://github.com/xia-lab/MetaboAnalystR.
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TL;DR: The MetaboAnalyst 5.0 as mentioned in this paper is the latest version of the web-based platform for comprehensive metabolomics data analysis and interpretation, aiming to narrow the gap from raw data to functional insights for global metabolomics based on HRMS.
Abstract: Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.
1,530 citations
TL;DR: An overview of the main functional modules and the general workflow of MetaboAnalyst 4.0 is provided, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc.
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.
1,522 citations
Cites methods from "MetaboAnalystR: an R package for fl..."
...To address these issues, we have developed MetaboAnalystR (Chong et al., 2019; Chong & Xia, 2018)—a companion R package based on the R codebase underlying the MetaboAnalyst web server....
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...…also been completely re-engineered to display underlying R commands in real time, which can be used in conjunction with our companion MetaboAnalystR (Chong & Xia, 2018; Chong, Yamamoto, & Xia, 2019) package to enable more flexible and reproducible metabolomics data analysis and batch processing....
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TL;DR: This work introduces MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: efficient parameter optimization for peak picking; automated batch effect correction; and more accurate pathway activity prediction that offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
Abstract: Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
320 citations
Cites methods from "MetaboAnalystR: an R package for fl..."
...To support the broad R user community, previous versions of MetaboAnalystR [5,23] implemented mummichog v1....
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TL;DR: MetaboAnalyst 5.0 as discussed by the authors extends the previous 2011 Nature Protocol by providing stepwise instructions to optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata.
Abstract: Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.
279 citations
TL;DR: This work introduces MetaboAnalystR 2.0, a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment and integrates XCMS and CAMERA to support raw spectral processing and peak annotation.
Abstract: Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.
234 citations
Cites background from "MetaboAnalystR: an R package for fl..."
...0), to help tackle issues associat d with workflow customization, repro ucibility, and handling large datasets [24]....
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References
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Journal Article•
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
272,030 citations
TL;DR: The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R to simplify model training and tuning across a wide variety of modeling techniques.
Abstract: The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.
5,144 citations
"MetaboAnalystR: an R package for fl..." refers methods in this paper
...MetaboAnalystR builds upon several R packages such as caret (Kuhn, 2008) for classification and performance evaluation, and ROCR (Sing et al., 2005) for visualizing biomarker performance....
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TL;DR: The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions.
Abstract: We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.
2,857 citations
TL;DR: UNLABELLED ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R that features over 25 performance measures that can be freely combined to create two-dimensional performance curves.
Abstract: Summary: ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage.
Availability:http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent.
Contact: tobias.sing@mpi-sb.mpg.de
2,838 citations
TL;DR: This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases, the growing role of metabolites in drug discovery and its potential effect on precision medicine.
Abstract: Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.
939 citations
"MetaboAnalystR: an R package for fl..." refers background in this paper
...It complements other omics technologies in multi-omics characterization of biological systems, and is poised to play a significant role in precision medicine (Wishart, 2016)....
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