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metaRbolomics packageverse
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| Table | Section | Functionalities | Package | Code_link | Reference | Repo | |
|---|---|---|---|---|---|---|---|
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | MS data handling | Parser for common file formats: mzXML, mzData, mzML and netCDF. Usually not used directly by the end user, but provides functions to read raw data for other packages. | mzR | https://doi.org/doi:10.18129/B9.bioc.mzR | [@chambers_2012] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | MS data handling | Infrastructure to manipulate, process and visualise MS and proteomics data, ranging from raw to quantitative and annotated data. | MSnbase | https://doi.org/doi:10.18129/B9.bioc.MSnbase | [@gatto_2012] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | MS data handling | Export and import of processed metabolomics MS results to and from the mzTab-M for metabolomics data format. | rmzTab-M | https://lifs-tools.github.io/rmzTab-m/index.html | [@hoffmann_2019] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | MS data handling | Converts MRM-MS (.mzML) files to LC-MS style .mzML. | MRMConverteR | https://github.com/wilsontom/MRMConverteR | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | MS data handling | Infrastructure for import, handling, representation and analysis of chromatographic MS data. | Chromatograms | https://github.com/RforMassSpectrometry/Chromatograms | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | MS data handling | Infrastructure for import, handling, representation and analysis of MS spectra. | Spectra | https://github.com/RforMassSpectrometry/Spectra | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Pre-processing and visualization for (LC/GC-)MS data. Includes visualization and simple statistics. | xcms | https://doi.org/doi:10.18129/B9.bioc.xcms | [@smith_2006; @tautenhahn_2008] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Automatic optimization of XCMS parameters based on isotopes. | IPO | http://bioconductor.org/packages/release/bioc/html/IPO.html | [@libiseller_2015] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Parameter tuning algorithm forXCMS, MZmine2, and other metabolomics data processingsoftware. | Autotuner | https://doi.org/doi:10.18129/B9.bioc.Autotuner | BioC | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Pre-processing and visualization for (LC/GC-)MS data. Includes visualization and simple statistics. | yamss | http://bioconductor.org/packages/release/bioc/html/yamss.html | [@myint_2017] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Peak picking with XCMS and apLCMS, low intensity peak detection via replicate analyses. Multi-parameter feature extraction and data merging, sample quality and feature consistency evaluation. Annotation with METLIN and KEGG. | xMSanalyzer | https://sourceforge.net/projects/xmsanalyzer/ | [@uppal_2013] | SF | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Pre-processing and alignment of LC-MS data without assuming a parametric peak shape model allowing maximum flexibility. It utilizes the knowledge of known metabolites, as well as robust machine learning. | apLCMS | http://web1.sph.emory.edu/apLCMS/ | [@yu_2014] | SF | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Peak detection using chromatogram subregion detection, consensus integration bound determination and Accurate missing value integration. Outputs in XCMS-compatible format. | warpgroup | https://github.com/nathaniel-mahieu/warpgroup | [@mahieu_2016] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Peak picking for (LC/GC-)MS data, improving the detection of low abundance signals via a master map of m/z/RT space before peak detection. Results are XCMS-compatible. | cosmiq | https://doi.org/doi:10.18129/B9.bioc.cosmiq | BioC | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | m/z detection (i.e. peak-picking) for accurate mass data, collecting all data points above an intensity threshold, grouping them by m/z values and estimating representative m/z values for the clusters; extracting EICs. | AMDORAP | http://amdorap.sourceforge.net/ | [@takahashi_2011] | SF | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | (GC/LC)-MS data analysis for environmental science, including raw data processing, analysis of molecular isotope ratios, matrix effects, and short-chain chlorinated paraffins. | enviGCMS | https://cran.r-project.org/web/packages/enviGCMS/index.html | [@yu_2017] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | Sequential partitioning, clustering and peak detection of centroided LC-MS mass spectrometry data (.mzXML), with Interactive result and raw data plot. | enviPick | https://cran.r-project.org/web/packages/enviPick/index.html | CRAN | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | PeakpickingwithXCMS. Groups chemically related features beforealignmentacross samples. Additional processing after alignment includes feature validation, re-integration and annotation based on custom database. | massFlowR | https://github.com/lauzikaite/massFlowR | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Peak picking, grouping and alignment (LC-MS focussed or general) | KPIC2 extracts pure ion chromatograms (PIC) via K-means clustering of ions in region of interest, performs grouping and alignment, grouping of isotopic and adduct features, peak filling and Random Forest classification. | KPIC2 | https://github.com/hcji/KPIC2 | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Isotope labeling using MS | Analysis of untargeted LC/MS data from stable isotope-labeling experiments. Also uses XCMS for feature detection. | geoRge | https://github.com/jcapelladesto/geoRge | [@capellades_2016] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Isotope labeling using MS | Correction of MS and MS/MS data from stable isotope labeling (any tracer isotope) experiments for natural isotope abundance and tracer impurity. Separate GUI available in IsoCorrectoRGUI. | IsoCorrectoR | https://bioconductor.org/packages/release/bioc/html/IsoCorrectoR.html | [@heinrich_2018] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Isotope labeling using MS | Extension of XCMS that provides support for isotopic labeling. Detection of metabolites that have been enriched with isotopic labeling. | X13CMS | http://pattilab.wustl.edu/software/x13cms/x13cms.php | [@huang_2014] | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Isotope labeling using MS | Analysis of isotopic patterns in isotopically-labeled MS data. Estimates the isotopic abundance of the stable isotope (either 2H or 13C) within specified compounds. | IsotopicLabelling | https://github.com/RuggeroFerrazza/IsotopicLabelling | [@ferrazza_2017] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Isotope labeling using MS | Finding the dual (or multiple) isotope labeled analytes using dual labeling of metabolites for metabolome analysis (DLEMMA) approach, described in Liron [42]. | Miso | https://cran.r-project.org/web/packages/Miso | [@dong_2019] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Peak picking using peak apex intensities for selected masses. Reference library matching, RT/RI conversion plus metabolite identification using multiple correlated masses. Includes GUI. | TargetSearch | http://bioconductor.org/packages/2.5/bioc/html/TargetSearch.html | [@cuadrosinostroza_2009] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Pre-processing for targeted (SIM) GC-MS data. Guided selection of appropriate fragments for the targets of interest by using an optimization algorithm based on user provided library. | SIMAT | https://doi.org/doi:10.18129/B9.bioc.SIMAT | [@ranjbar_2015] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Deconvolution of MS2 spectra obtained with wide isolation windows. | decoMS2 | http://pattilab.wustl.edu/software/decoms2/decoms2.php | [@nikolskiy_2013] | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Deconvolution of SWATH-MS experiments to MRM transitions. | SWATHtoMRM | http://www.metabolomics-shanghai.org/softwaredetail.php?id=128 | [@zha_2018] | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Automatic analysis of large scale MRM experiments. | MRMAnalyzer | http://www.metabolomics-shanghai.org/softwaredetail.php?id=34 | [@cai_2015] | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Tailors peak detection for targeted metabolites through iterative user interface. It automatically integrates peak areas for all isotopologues and outputs extracted ion chromatograms (EICs). | AssayR | https://github.com/stevehoang/assayr | [@wills_2017] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Targeted peak picking and annotation. Includes Shiny GUI. | peakPantheR | https://github.com/phenomecentre/peakPantheR | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Toolkit for working with Selective Reaction Monitoring (SRM) MS data and other variants of targeted LC-MS data. | sRm | https://github.com/wilsontom/sRm | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Deconvolution of SWATH-MS data. | DecoMetDIA | https://github.com/ZhuMSLab/DecoMetDIA | [@yin_2019] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Targeted MS | Targeted peak picking and annotation. All functions through Shiny GUI. | TarMet | https://github.com/hcji/TarMet | GitHub | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Unsupervised data mining on GC-MS. Clustering of mass spectra to detect compound spectra. The output can be searched in NIST and ARISTO [50]. | MSeasy | https://cran.r-project.org/package=MSeasy | [@nicol_2012] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Pre-processing for GC/MS, MassBank search, NIST format export. | erah | https://cran.r-project.org/package=erah | [@domingoalmenara_2016] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Pre-processing using AMDIS [53, 54] for untargeted GC-MS analysis. Feature grouping across samples, improved quantification, removal of false positives, normalisation via internal standard or biomass; basic statistics. | Metab | https://doi.org/doi:10.18129/B9.bioc.Metab | [@aggio_2011] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Deconvolution of GC-MS and GC×GC-MS unit resolution data using orthogonal signal deconvolution (OSD), independent component regression (ICR) and multivariate curve resolution (MCR-ALS). | osd | http://cran.r-project.org/package=osd | [@domingoalmenara_2015; @domingoalmenara_2016a] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Corrects overloaded signals directly in raw data (from GC-APCI-MS) automatically by using a Gaussian or isotopic-ratio approach. | CorrectOverloadedPeaks | https://cran.r-project.org/package=CorrectOverloadedPeaks | [@lisec_2016] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Alignment of GC data. Also GC-FID or any single channel data since it works directly on peak lists. | GCalignR | https://cran.r-project.org/package=GCalignR | [@ottensmann_2018] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | GC-MS data processing and compound annotation pipeline. Includes the building, validating, and query of in-house databases. | metaMS | https://www.bioconductor.org/packages/release/bioc/html/metaMS.html | [@wehrens_2014] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Peak picking for GC×GC-MS using bayes factor and mixture probability models. | msPeak | http://mrr.sourceforge.net/download.html | [@kim_2014] | SF | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Peak alignment for GC×GC-MS data with homogeneous peaks based on mixture similarity measures. | mSPA | http://mrr.sourceforge.net/download.html | [@kim_2011a] | SF | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Peak alignment for GC×GC-MS data with homogeneous and/or heterogenous peaks based on mixture similarity measures. | SWPA | http://mrr.sourceforge.net/download.html | [@kim_2011] | SF | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Chemometrics analysis GC×GC-MS: baseline correction, smoothing, COW peak alignment, multiway PCA is incorporated. | RGCxGC | https://cran.r-project.org/package=RGCxGC | CRAN | ||
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | GC-MS and GC×GC-MS | Retention time and mass spectra similarity threshold-free alignments, seamlessly integrates retention time standards for universally reproducible alignments, performs common ion filtering, and provides compatibility with multiple peak quantification methods. | R2DGC | https://github.com/rramaker/R2DGC | [@ramaker_2017] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Flow injection / direct infusion analysis | Pre-processing of data from Flow Injection Analysis (FIA) coupled to High-Resolution Mass Spectrometry (HRMS). | proFIA | https://doi.org/doi:10.18129/B9.bioc.proFIA | [@delabrire_2017] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Flow injection / direct infusion analysis | Flow In-jection Electrospray Mass Spectrometry Processing: data processing, classification modelling and variable selection in metabolite fingerprinting | FIEmspro | https://github.com/aberHRML/FIEmspro | [@enot_2008] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Flow injection / direct infusion analysis | Processing Mass Spectrometry spectrum by using wavelet based algorithm. Can be used for direct infusion experiments. | MassSpecWavelet | http://bioconductor.org/packages/release/bioc/html/MassSpecWavelet.html | [@du_2006] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Filtering of features originating from artifactual interference. Based on the analysis of an extract of E. coli grown in 13C-enriched media. | credential | https://github.com/pattilab/credential | [@mahieu_2014] | GitHub | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Wrappers for XCMS and CAMERA. Also includes matching to a spectral library and a GUI. | metaMS | https://doi.org/doi:10.18129/B9.bioc.metaMS | [@wehrens_2014] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Processing of peaktables from AMDIS, XCMS or ChromaTOF. Functions for plotting also provided. | flagme | https://doi.org/doi:10.18129/B9.bioc.flagme | [@robinson_2007] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Parametric Time Warping (RT correction) for both DAD and LC-MS. | ptw | https://cran.r-project.org/package=ptw | [@wehrens_2015] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | R wrapper for X!Tandem software for protein identification. | rTANDEM | http://bioconductor.org/packages/release/bioc/html/rTANDEM.html | [@fredericfournier_2017] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Building, validation, and statistical analysis of extended assay libraries for SWATH proteomics data. | SwathXtend | https://bioconductor.org/packages/release/bioc/html/SwathXtend.html | [@pascovici_2017] | BioC | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Split a data set into a set of likely true metabolites and likely measurement artifacts by comparing missing rates of pooled plasma samples and biological samples. | MetProc | https://cran.r-project.org/package=MetProc | [@chaffin_2019] | CRAN | |
| Table 1: R packages for mass spectrometry data handling and (pre-)processing. | Other | Quality of LC-MS and direct infusion MS data. Generates a report that contains a comprehensive set of quality control metrics and charts. | qcrms | https://github.com/computational-metabolomics/qcrms | GitHub | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Molecular formula and isotope analysis | Simulation of and decomposition of Isotopic Patterns. | Rdisop | http://bioconductor.org/packages/release/bioc/html/Rdisop.html | [@böcker_2008] | BioC | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Molecular formula and isotope analysis | Calculation of isotope fine patterns. Also adduct calculations and molecular formula parsing. Web version available at www.envipat.eawag.ch. | enviPat | https://cran.r-project.org/package=enviPat | [@loos_2015] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Molecular formula and isotope analysis | Molecular formula assignment, mass recalibration, signal-to-noise evaluation, and unambiguous formula selections are provided. | MFAssignR | https://github.com/ChARM-Group/MFAssignR | GitHub | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Molecular formula and isotope analysis | Uses GenForm for molecular formula generation on mass accuracy, isotope and/or MS/MS fragments, as well as performing MS/MS subformula annotation. | GenFormR | https://github.com/schymane/GenFormR | [@meringer_2011] | GitHub | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Molecular formula and isotope analysis | Checking element isotopes, calculating (isotope labelled) exact monoisotopic mass,m/zvalues, mass accuracy, and inspecting possible contaminant mass peaks, examining possible adducts in ESI and MALDI. | MSbox | https://cran.r-project.org/package=MSbox | CRAN | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Grouping of correlated features into pseudo compound spectra using correlation across samples and similarity of peak shape. Annotation of isotopes and adducts. Works as an add-on to XCMS. | CAMERA | https://doi.org/doi:10.18129/B9.bioc.CAMERA | [@kuhl_2012] | BioC | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Grouping of features based on similarity between coelution profiles. | CliqueMS | https://cran.r-project.org/web/packages/cliqueMS/ | [@senan_2019] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Cluster based feature grouping for non-targeted GC or LC-MS data. | RAMClustR | https://github.com/cbroeckl/RAMClustR | [@broeckling_2014] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Uses dynamic block summarisation to group features belong to the same compound. Correction for peak misalignments and isotopic pattern validation. | MetTailor | https://sourceforge.net/projects/mettailor/ | [@G. chen_2015] | SF | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Isotope & adduct peak grouping, homologous series detection. | nontarget | https://cran.r-project.org/web/packages/nontarget/ | [@loos_2017] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Bayesian approach for grouping peaks originating from the same compound. | peakANOVA | http://research.cs.aalto.fi/pml/software/peakANOVA/ | [@suvitaival_2014] | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Combination of data from positive and negative ionization mode finding common molecular entities. | MScombine | https://cran.r-project.org/package=MScombine | [@calderón-santiago_2016] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Grouping of correlated features into pseudo compound spectra using correlation across sample. Annotation of isotopes and adducts. Can work directly with the XCMS output. | Astream | http://www.urr.cat/AStream/AStream.html | [@alonso_2011] | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Navigation of high-resolution MS/MS data in a GUI based on mass spectral similarity. | MetCirc | https://bioconductor.org/packages/release/bioc/html/MetCirc.html | [@naake_2017] | BioC | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | MS feature grouping | Deconvolution of MS/MS spectra obtained with wide isolation windows. | decoMS2 | http://pattilab.wustl.edu/software/decoms2/decoms2.php | [@nikolskiy_2013] | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Bayesian probabilistic annotation. | ProbMetab | https://github.com/rsilvabioinfo/ProbMetab | [@silva_2014] | GitHub | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Isotope & adduct peak grouping, unsupervised homologous series detection. | nontarget | https://cran.r-project.org/web/packages/nontarget/ | [@loos_2017] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Automatic interpretation of fragments and adducts in MS spectra. Molecular formula prediction based on fragmentation. | InterpretMSSpectrum | https://cran.r-project.org/package=InterpretMSSpectrum | [@jaeger_2017] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Automated annotation using MS2 data or databases and retention time. Calculation of spectral and chemical networks. | compMS2Miner | https://github.com/WMBEdmands/compMS2Miner | [@William M B edmands_2017] | GitHub | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Screening, annotation, and putative identification of mass spectral features in lipidomics. Default databases contain ~25,000 compounds. | LOBSTAHS | https://doi.org/doi:10.18129/B9.bioc.LOBSTAHS | [@collins_2016] | BioC | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Automated annotation of fragments from MS and MS2 and putative identification against simulated library fragments of ~500,000 lipid species across ~60 lipid types. | LipidMatch | https://github.com/GarrettLab-UF/LipidMatch | [@koelmel_2017] | GitHub | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Annotation of lipid type and acyl groups on independent acquisition-mass spectrometry lipidomics based on fragmentation and intensity rules. | LipidMS | https://cran.r-project.org/package=LipidMS | [@alcoriza-balaguer_2018] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Accurate mass and/or retention time and/or collisional cross section matching. | masstrixR | https://github.com/michaelwitting/masstrixR | [@wägele_2012] | GitHub | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Downloads KEGG compounds orthology data and wraps the KEGGREST package to extractgene data. | omu | https://cran.r-project.org/package=omu | [@tiffany_2019] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Paired mass distance analysis to find independent peaks inm/z-retention time profiles based on retention time hierarchical cluster analysis and frequency analysis of paired mass distances within retention time groups. Structure directed analysis to find potential relationship among those independent peaks. Shiny GUI included. | pmd | https://cran.r-project.org/package=pmd | [@M. yu_2019] | CRAN | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | A Bayesian annotation method for LC/MS data integrating biochemical relations, isotope patterns and adduct formation. | IPA | https://github.com/francescodc87/IPA | [@DelCarratore_2019] | GitHub | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | Tools for processing raw data to database ready cleaned spectra with metadata. | RMassBank | https://www.bioconductor.org/packages/release/bioc/html/RMassBank.html | [@stravs_2013] | BioC | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | From RT-m/z pairs (or m/z alone) creates MS2 experiment files with non-overlapping subsets of the targets. Bruker, Agilent and Waters supported. | MetShot | https://github.com/sneumann/MetShot | [@neumann_2013] | GitHub | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | Creating MS libraries from LC-MS data using XCMS/CAMERA packages. A multi-modular annotation function including X-Rank spectral scoring matches experimental data against the generated MS library. | MatchWeiz | https://github.com/AharoniLab/MatchWeiz | [@shahaf_2016] | GitHub | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | Assess precursor contribution to fragment spectrum acquired or anticipated isolation windows using "precursor purity" for both LC-MS(/MS) and DI-MS(/MS) data. Spectral matching against a SQLite database of library spectra. | msPurity | https://doi.org/doi:10.18129/B9.bioc.msPurity | [@lawson_2017] | BioC | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | Automated quantification of metabolites by targeting mass spectral/retention time libraries into full scan-acquired GC-MS chromatograms. | baitmet | https://cran.r-project.org/package=baitmet | [@domingo-almenara_2017] | CRAN | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | MS2 spectra similarity and unsupervised statistical methods. Workflow from raw data to visualisations and is interfaceable with XCMS. | CluMSID | https://bioconductor.org/packages/CluMSID/ | [@depke_2019] | BioC | |
| Table 3: R packages for MS/MS data. | MS2 and libraries | Import of spectra from different file formats such as NIST msp, mgf (mascot generic format), and library (Bruker) to MSnbase objects. | MSnio | https://github.com/meowcat/MSnio | GitHub | ||
| Table 3: R packages for MS/MS data. | MS2 and libraries | Multi-purpose mass spectrometry package. Contains many different functions .e.g. isotope pattern calculation, spectrum similarity, chromatogram plotting, reading of msp files and peptide related functions. | OrgMassSpecR | https://cran.r-project.org/package=OrgMassSpecR | CRAN | ||
| Table 3: R packages for MS/MS data. | MS2 and libraries | Annotation of LC-MS data based on a database of fragments. | MetaboList | https://cran.r-project.org/package=MetaboList | [@sentandreu_2018] | CRAN | |
| Table 3: R packages for MS/MS data. | In silico fragmentation | In silico fragmentation of candidate structures. | MetFragR | https://github.com/c-ruttkies/MetFragR | [@ruttkies_2016] | GitHub | |
| Table 3: R packages for MS/MS data. | In silico fragmentation | SOLUTIONS for High ReSOLUTION Mass Spectrometry including several functions to interact with MetFrag, developed during the SOLUTIONS project (www.solutions-project.eu). | ReSOLUTION | https://github.com/schymane/ReSOLUTION | [@ruttkies_2016] | GitHub | |
| Table 3: R packages for MS/MS data. | In silico fragmentation | Uses MetFrag and adds substructure prediction using the isotopic pattern. Can be trained on a custom dataset. | CCC | https://github.com/lucanard/CCC | [@narduzzi_2018] | GitHub | |
| Table 3: R packages for MS/MS data. | Retention time correction | Retention time prediction based on compound structuredescriptors. Five different machine learning algorithms are available to build models. Plotting available to explore chemical space and model quality assessment. | Retip | https://github.com/PaoloBnn/Retip | GitHub | ||
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data processing and Analysis | A tool for processing of 1H NMR data including: Apodization, baseline correction, bucketing, Fourier transformation, warping and phase correction. Bruker FID can be directly imported. | PepsNMR | https://github.com/ManonMartin/SOAP-NMR | [@martin_2017] | GitHub | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data processing and Analysis | Spectra alignment, peak picking based processing, Quantitative analysis and visualizations for 1D NMR. | speaq | https://cran.r-project.org/package=speaq | [@beirnaert_2017; @vu_2011] | CRAN | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data processing and Analysis | Interactive environment based on R-Shiny that includes a complete set of tools to process and visualize 1D NMR spectral data. Processing includes baseline correction, ppm calibration, removal of solvents and contaminants and re-alignment of chemical shifts. | NMRProcFlow | https://www.nmrprocflow.org/ | [@D. jacob_2017] | Bitbucket | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data processing and Analysis | TheMetaboMateR toolbox covers basic processing andstatisticalanalysis steps including; several spectral quality assessment (such as dealing with baseline distortions, water suppression to quality assessment of shimming and line width) as well as pre-processing (referencing,baselinecorrection, ... ) to multivariate analysis statistics functions. | MetaboMate | https://github.com/kimsche/MetaboMate | GitHub | ||
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data Analysis and Identification | Analysis of 1D and 2D NMR spectra using a ROIs based approach. Export to MMCD or uploaded to BMRB for identification. | rNMR | http://rnmr.nmrfam.wisc.edu/ | [@lewis_2009] | ||
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data Analysis and Identification | Pre-processing and identification in an R-based GUI for 1D NMR. | rDolphin | https://github.com/danielcanueto/rDolphin | [@cañueto_2018] | GitHub | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data Analysis and Identification | Bayesian automated metabolite analyser for 1D NMR spectra. Deconvolution of NMR spectra and automate metabolite quantification. Also identification based on chemical shift lists. | BATMAN | http://batman.r-forge.r-project.org/ | [@hao_2012] | RF | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data Analysis and Identification | “ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra.” | ASICS | https://bioconductor.org/packages/release/bioc/html/ASICS.html | [@lefort_2019] | BioC | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data Analysis and Identification | ASICSdata: 1D NMR spectra for ASICS. | ASICSdata | https://www.bioconductor.org/packages/release/data/experiment/vignettes/ASICSdata/inst/doc/ASICSdata.html | [@lefort_2019] | BioC | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | Data Analysis and Identification | shiny-based interactive NMR data import and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses. | iSTATS | https://cran.r-project.org/package=iSTATS | CRAN | ||
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | NMR and integration with Genomics | MWASTools: an integrated pipeline to perform NMR based metabolome-wide association studies (MWAS). Quality control analysis; MWAS using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using STOCSY; and biological interpretation of MWAS results. | MWASTools | https://bioconductor.org/packages/release/bioc/html/MWASTools.html | [@rodriguez-martinez_2018] | BioC | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | NMR and integration with Genomics | An Integrated Suite for Genetic Mapping of Quantitative Variations of 1H NMR-Based Metabolic Profiles. mQTL-NMR provides a complete metabotype quantitative trait locus (mQTL) mapping analysis pipeline for metabolomic data. | mQTL.NMR | https://doi.org/doi:10.18129/B9.bioc.mQTL.NMR | [@hedjazi_2015] | BioC | |
| Table 4: R packages for NMR data handling, (pre-)processing and analysis. | NMR and integration with Genomics | Handles hyperspectral data, i.e. spectra plus further information such as spatial information, time, concentrations, etc. Such data are frequently encountered in Raman, IR, NIR, UV/VIS, NMR, MS, etc. | hyperSpec | https://cran.r-project.org/package=ChemoSpec | CRAN | ||
| Table 5: R Packages for UV data handling and (pre-)processing. | DAD | Multivariate Curve Resolution (Alternating Least Squares) for DAD data. | alsace | https://github.com/rwehrens/alsace | [@wehrens, carvalho_2015] | GitHub | |
| Table 5: R Packages for UV data handling and (pre-)processing. | DAD | Parametric Time Warping (RT correction) for both DAD and LC-MS. | ptw | https://cran.r-project.org/package=ptw | [@wehrens, bloemberg_2015] | CRAN | |
| Table 5: R Packages for UV data handling and (pre-)processing. | DAD | Handles hyperspectral data, i.e. spectra plus further information such as spatial information, time, concentrations, etc. Such data are frequently encountered in Raman, IR, NIR, UV/VIS, NMR, MS, etc. | hyperSpec | https://cran.r-project.org/package=ChemoSpec | CRAN | ||
| Table 5: R Packages for UV data handling and (pre-)processing. | DAD | Projection based methods for preprocessing, exploring and analysis of multivariate data. | mdatools | https://cran.r-project.org/package=mdatools | CRAN | ||
| Table 5: R Packages for UV data handling and (pre-)processing. | DAD | Collection of baseline correction algorithms, along with a GUI for optimising baseline algorithm parameters. | baseline | https://cran.r-project.org/package=baseline | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Sample size | Estimate sample sizes for metabolomics experiments, (NMR and targeted approaches supported). | MetSizeR | https://cran.r-project.org/package=MetSizeR | [@nyamundanda_2013] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Cross-contribution robust multiple standard normalization. Normalization using internal standards. | crmn | https://cran.r-project.org/web/packages/crmn/index.html | [@redestig_2009] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Within and between batch correction of LC-MS metabolomics data using either QC samples or all samples. | batchCorr | https://gitlab.com/CarlBrunius/batchCorr | [@brunius_2016] | GitLab | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Normalisation for low concentration metabolites. Mixed model with simultaneous estimation of a correlation matrix. | Metabnorm | https://sourceforge.net/projects/metabnorm/ | [@jauhiainen_2014] | SF | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | A collection of data distribution normalization methods. | Normalizer | http://quantitativeproteomics.org/normalyzer | [@chawade_2014] | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Functions for drift removal and data normalisation based on: component correction, median fold change, ComBat or common PCA (CPCA). | intCor | http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/ | [@fernández-albert, llorach, garcia-aloy_2014] | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Normalisation using a singular value decomposition. | EigenMS | https://sourceforge.net/projects/eigenms/ | [@karpievitch_2014] | SF | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Normalization based on RUV-random [164]. | MetNorm | https://cran.r-project.org/web/packages/MetNorm/index.html | [@de livera_2015] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | SVR-based normalization and integration for large-scale metabolomics data. | MetNormalizer | http://www.metabolomics-shanghai.org/softwaredetail.php?id=39 | [@shen_2016] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Drift correction using QC samples or all study samples. | BatchCorrMetabolomics | https://github.com/rwehrens/BatchCorrMetabolomics | [@wehrens_2016] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Signal and Batch Correction for Mass Spectrometry | SBCMS | https://github.com/computational-metabolomics/sbcms | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Multiple fitting models to correct intra- and inter-batch effects. | MetaboQC | https://cran.r-project.org/package=MetaboQC | [@calderón-santiago_2017] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Normalization | Collection of functions designed to implement, assess, and choose a suitable normalization method for a given metabolomics study. | NormalizeMets | https://cran.r-project.org/package=NormalizeMets | [@de livera_2018] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Exploratory Data Analysis | A large number of methods available for PCA. | pcaMethods | http://bioconductor.org/packages/release/bioc/html/pcaMethods.html | [@stacklies_2007] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Exploratory Data Analysis | Chemometric analysis of NMR, IR or Raman spectroscopy data. It includes functions for spectral visualisation, peak alignment, HCA, PCA and model-based clustering. | ChemoSpec | https://cran.r-project.org/package=ChemoSpec | BioC | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Exploratory Data Analysis | Joint analysis of MS and MS2 data, where hierarchical cluster analysis is applied to MS2 data to annotate metabolite families and principal component analysis is applied to MS data to discover regulated metabolite families. | MetFamily | https://github.com/ipb-halle/MetFamily | [@treutler_2016] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Univariate hypothesis testing | Many methods for corrections for multiple testing. | multtest | http://bioconductor.org/packages/release/bioc/html/multtest.html | [@pollard_2005] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Univariate hypothesis testing | Estimate tail area-based false discovery rates (FDR) as well as local false discovery rates (fdr) for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). | fdrtool | http://strimmerlab.org/software/fdrtool/ | [@strimmer_2008] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Univariate hypothesis testing | GUI for statistical analysis using linear mixed models to normalize data and ANOVA to test for treatment effects. | MetabR | http://metabr.r-forge.r-project.org/ | [@ernest_2012] | RF | |
| Table 6: R packages for statistical analysis of metabolomics data. | Univariate hypothesis testing | Derives stable estimates of the metabolome-wide significance level within a univariate approach based on a permutation procedure which effectively controls the maximum overall type I error rate at the α level. | MWSL | https://github.com/AlinaPeluso/MWSL | [@peluso_2018] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Various multivariate methods to analyze metabolomics datasets. Main methods include PCA, Partial Least Squares regression (PLS), and extensions to the PLS like PLS Discriminant Analysis PLS-DA and the orthogonal variants OPLS(-DA). | ropls | http://bioconductor.org/packages/release/bioc/html/ropls.html | [@thévenot_2015] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Package for performing Partial Least Squares regression (PLS). | pls | https://cran.r-project.org/package=pls | [@mevik_2007] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | PPCA, PPCCA, MPPCA. | MetabolAnalyze | https://cran.r-project.org/package=MetabolAnalyze | [@nyamundanda_2010] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | General framework for building regression and classification models. | caret | https://cran.r-project.org/package=caret | [@kuhn_2008] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | ASCA, figure of merit (FoM), PCA, Goeman’s global test for metabolomic pathways (Q-stat), Penalized Jacobian method (for calculating network connections), Time-lagged correlation method and zero slopes method. It also includes centering and scaling functions. | MetStaT | https://cran.r-project.org/package=MetStaT | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | RF for the construction, optimization and validation of classification models with the aim of identifying biomarkers. Also normalization, scaling, PCA, MDS. | RFmarkerDetector | https://cran.r-project.org/package=RFmarkerDetector | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | PLS-DA, RF, SVM, GBM, GLMNET, PAM. | OmicsMarkeR | http://bioconductor.org/packages/release/bioc/html/OmicsMarkeR.html | [@determan jr_2014] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Recursive feature elimination approach that selects features which significantly contribute to the performance of PLS-DA, Random Forest or SVM classifiers. | biosigner | http://bioconductor.org/packages/release/bioc/html/biosigner.html | [@rinaudo_2016] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Find Biomarkers in two class discrimination problems with variable selection methods provided for several classification metods (lasso/elastic net, PC-LDA, PLS-DA, and several t-tests). | BioMark | https://cran.r-project.org/web/packages/BioMark/index.html | [@wehrens_2012] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Unsupervised feature extraction specifically designed for analysing noisy and high-dimensional datasets. | KODAMA | https://cran.r-project.org/package=KODAMA | [@cacciatore_2017] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Non-parametric method for identifying differentially expressed features based on the estimated percentage of false predictions. | RankProd | https://doi.org/doi:10.18129/B9.bioc.RankProd | [@del carratore_2017] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Fits multi-way component models via alternating least squares algorithms with optional constraints: orthogonal, non-negative, unimodal, monotonic, periodic, smooth, or structure. Fit models include InDScal, PARAFAC, PARAFAC2, SCA, Tucker. | multiway | https://cran.r-project.org/package=multiway | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Decompose a tensor of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD is also available. Also includes PCAn (Tucker-n) and PARAFAC/CANDECOMP. | PTAk | https://cran.r-project.org/package=PTAk | [@leibovici_2010] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include Individual Differences Scaling, Multiway Covariates Regression, PARAFAC (1 and 2), SCA, and Tucker Factor Analysis. | ThreeWay | https://cran.r-project.org/package=ThreeWay | [@giordani_2014] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Performs variable selection in a multivariate linear model by estimating the covariance matrix of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion. | MultiVarSel | https://cran.r-project.org/package=MultiVarSel | [@perrot-dockès_2018] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Performs the O2PLS data integration method for two datasets yielding joint and data-specific parts for each dataset. | OmicsPLS | https://cran.r-project.org/package=OmicsPLS | [@bouhaddani_2018] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Contains ordination methods such as ReDundancy Analysis (RDA), (Canonical or Detrended) Correspondence Analysis (CCA, DCA for binary explanatory variables), (Non-metric) Multi-Dimensional Scaling ((N)MDS) and other univariate and multivariate methods. Originally developed for vegetation ecologists, many functions are also applicable to metabolomics. | vegan | https://cran.r-project.org/package=vegan | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Linear and non-linear Discriminant Analysis methods (e.g. LDA), stepwise selection and classification methods useful for feature selection. | klaR | https://cran.r-project.org/package=klaR | [@weihs_2005] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Variable selection methods for PLS, including significance multivariate correlation (SMC), selectivity ratio (SR), variable importance in projections (VIP), loading weights (LW), and regression coefficients (RC). It contains also some other modelling methods. | plsVarSel | https://cran.r-project.org/package=plsVarSel | [@mehmood_2012] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Predictive multivariate modelling using PLS and Random Forest Data. Repeated double cross unbiased validation and variable selection. | MUVR | https://gitlab.com/CarlBrunius/MUVR | [@shi_2019] | GitLab | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Biomarker validation for predicting survival. Cross validation methods to validate and select biomarkers when the outcome of interest is survival. | MetabolicSurv | https://cran.r-project.org/package=MetabolicSurv | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Pre-treatment, classification, feature selection and correlation analyses of metabolomics data. | metabolyseR | https://github.com/jasenfinch/metabolyseR | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. | packMBPLSDA | https://cran.r-project.org/package=packMBPLSDA | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multivariate modeling and feature selection | Robust identification of time intervals are significantly different between groups. | OmicsLonDA | https://doi.org/doi:10.18129/B9.bioc.OmicsLonDA | BioC | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | Multiple co-inertia analysis of omics datasets (MCIA) is a multivariate approach for visualization and integration of multi-omics datasets. The MCIA method is not dependent on feature annotation therefore can extract important features even when there are not present across all datasets. | omicade4 | http://bioconductor.org/packages/release/bioc/html/omicade4.html | [@meng_2014] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | STATegRa combines information in multiple omics datasets to evaluate the reproducibility among samples and across experimental condition using component analysis (omicsNPC implements the NonParametric Combination) and clustering. | STATegRa | https://doi.org/doi:10.18129/B9.bioc.STATegRa | [@hernández-de-diego_2014] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | Statistical framework supporting many different types of multivariate analyses (e.g. PCA, PLS, CCA, PLS-DA, etc.). | mixOmics | https://cran.r-project.org/web/packages/mixOmics/index.html | [@K.-A. lê cao_2009; @rohart_2017] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | STatistics in R Using Class Templates - Classes for building statistical workflows using methods, models and validation objects. Provides mechanism for STATO integration. | STRUCT | http://www.github.com/computational-metabolomics/struct | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | Multi-omics base clases integrable with commonly used R Bioconductor objects for omics data; container that holds omics results. | MultiDataSet | https://bioconductor.org/packages/release/bioc/html/MultiDataSet.html | [@hernandez-ferrer_2017] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | Identifies analyte-analyte (e.g. gene-metabolite) pairs whose relationship differs by phenotype (e.g. positive correlation in one phenotype, negative or no correlation in another). The software is also accessible as a user-friendly interface at intlim.bmi.osumc.edu. | IntLIM | https://github.com/mathelab/IntLIM | [@siddiqui_2018] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Mixture-model for accounting for data missingness'. | metabomxtr | https://doi.org/doi:10.18129/B9.bioc.metabomxtr | [@nodzenski_2014] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Kernel-Based Metabolite Differential Analysis provides a kernel-based score test to cluster metabolites between treatment groups, in order to handle missing values. | KMDA | https://cran.r-project.org/web/packages/KMDA/ | [@zhan_2015] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Visualization and imputation of missing values. VIM provides methods for the evaluation and visualization of the type and patterns of missing data. The included imputation approaches are kNN, Hot-Deck, iterative robust model-based imputation (IRMI), fast matching/imputation based on categorical variables and regression imputation. | VIM | https://cran.r-project.org/web/packages/VIM/index.html | [@kowarik_2016] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Graphical user interface for VIM. | VIMGUI | https://cran.r-project.org/web/packages/VIMGUI/VIMGUI.pdf | CRAN | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | kNN based imputation for microarray data. | impute | https://www.bioconductor.org/packages//2.10/bioc/html/impute.html | [@james_2013] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Bootstrap based algorithm and diagnostics for fast and robust multiple imputation for cross sectional, time series or combined cross sectional and time series data. | Amelia | https://cran.r-project.org/web/packages/Amelia/Amelia.pdf | [@honaker_2011] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Algorithms and diagnostics for the univariate imputation of time series data. | imputeTS | https://cran.r-project.org/package=imputeTS | [@moritz_2017] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Methods for the Imputation of incomplete continuous or categorical datasets. missMDA allows missing data imputation using in categorial, continuous or mixed-type datasets using PCA, CA, a multiple correspondence analysis (MCA) model, a multiple factor analysis (MFA) model or factorial analysis for mixed data (FAMD). | missMDA | https://cran.r-project.org/package=missMDA | [@josse_2016] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Random forest based missing data imputation for mixed-type, nonparametric data. An out-of-bag (OOB) error estimate is used for model optimization. | missForest | https://cran.r-project.org/web/packages/missForest/missForest.pdf | [@stekhoven_2012] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Multivariate imputation by chained equations using fully conditional specifications (FCS) for categorical, continuous and binary datasets. It includes various diagnostic plots for the evaluation of the imputation quality. | mice | https://cran.r-project.org/web/packages/mice/mice.pdf | [@buuren_2011] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Missing data imputation using an approximate Bayesian framework. Diagnostic algorithms are included to analyze the models, the assumptions of the imputation algorithm and the multiply imputed datasets. | mi | https://cran.r-project.org/package=mi | [@su_2011] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Missing value imputation | Iterative Gibbs sampler based left-censored missing value imputation. | GSimp | https://github.com/WandeRum/GSimp | [@wei, wang, jia_2018] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Missing value imputation, filtering, normalisation and averaging of technical replications. | MSPrep | https://sourceforge.net/projects/msprep/ | [@hughes_2014] | SF | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | HCA, Fold change analysis, heat maps, linear models (ordinary and empirical Bayes), PCA and volcano plots. Also log transformation, missing value replacement and methods for normalisation. Cross-contribution compensating multiple internal standard normalisation (ccmn) and remove unwanted variation (ruv2). | metabolomics | https://cran.r-project.org/package=metabolomics | [@de livera_2012] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Data processing, normalization, statistical analysis, metabolite set enrichment analysis, metabolic pathway analysis, and biomarker analysis. | MetaboAnalystR | https://github.com/xia-lab/MetaboAnalystR | [@chong_2018; @xia_2015] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Pipeline for metabolomics data pre-processing, with particular focus on data representation using univariate and multivariate statistics. Built on already published functions. | muma | https://cran.r-project.org/web/packages/muma/index.html | [@gaude_2013] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Framework for multiomics experiments. Identifies sources of variability in the experiment and performs additional analysis (identification of subgroups, data imputation, outlier detection). | MOFA | https://www.bioconductor.org/packages/release/bioc/html/MOFA.html | [@argelaguet_2018] | BioC | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Performs entry-level differential analysis on metabolomics data. | MetaboDiff | https://github.com/andreasmock/MetaboDiff | [@mock_2018] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | STRUCT wrappers for filtering, normalisation, missing value imputation, glog transform, HCA, PCA, PLSDA, PLSR, t-test, fold-change, ANOVA, Mixed Effects, post-hoc tests | STRUCTToolbox | https://github.com/computational-metabolomics/structToolbox | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Data transformation, filtering of feature and/or samples and data normalization. Quality control processing, statistical analysis and visualization of MS data. | pmartR | https://github.com/pmartR/pmartR | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Quality control, signal drift and batch correction, transformation, univariate hypothesis testing. | phenomis | https://github.com/ethevenot/phenomis | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Missing value filtering and imputation, zero value filtering, data normalization, data integration, data quality assessment, univariate statistical analysis, multivariate statistical analysis such as PCA and PLS-D and potential marker selection | MetCleaning | https://github.com/MetabLAB/MetCleaning | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Univariate analysis (linear model), PCA, clustered heatmap, and partial correlation network analysis. Based on classes from the Metabase package(Zhu 2019). | ShinyMetabase | https://github.com/zhuchcn/ShinyMetabase | GitHub | ||
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Outlier detection, PCA, drift correction,visualization, missing value imputation,classification. | MetabolomicsBasics | https://cran.r-project.org/package=MetabolomicsBasics | [@jaeger_2018] | CRAN | |
| Table 6: R packages for statistical analysis of metabolomics data. | Multiple workflow steps | Pre-processing, differential compound identification and grouping, traditional PK parameters calculation, multivariate statistical analysis, correlations, cluster analyses and resulting visualization. | polyPK | https://cran.r-project.org/package=polyPK | [@M. li_2018] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Network infrastructure and analysis | Infrastructure for representation of networks, analysis and visualization. | igraph | https://cran.r-project.org/package=igraph | @csardi_2006] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Network infrastructure and analysis | Infrastructure for representation of networks, analysis and visualization. | tidygraph | https://cran.r-project.org/package=tidygraph | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Network infrastructure and analysis | Infrastructure for representation of networks, analysis and visualization. | statnet | https://cran.r-project.org/package=statnet | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Network infrastructure and analysis | Interactive visualization and manipulation of networks. | RedeR | https://bioconductor.org/packages/release/bioc/html/RedeR.html | [@castro_2012] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Network infrastructure and analysis | Comparison of correlation networks from two experiments. | DiffCorr | https://cran.r-project.org/package=DiffCorr | [@fukushima_2013] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Network infrastructure and analysis | Correlation-based networks from metabolomics data and analysis tools. | BioNetStat | https://bioconductor.org/packages/release/bioc/html/BioNetStat.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Annotation | Putative annotation of unknowns in MS1 data. | MetNet | http://bioconductor.org/packages/release/bioc/html/MetNet.html | [@naake_2019] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Annotation | Putative annotation of unknowns in MS1 data. | xMSAnnotator | https://sourceforge.net/projects/xmsannotator/ | [@uppal_2017] | SF | |
| Table 8: R packages for network analysis and Biochemical pathways. | Annotation | Putative annotation of unknowns using MS1 and MS2 data. | MetDNA | https://github.com/ZhuMSLab/MetDNA | [@shen_2019] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Annotation | Visualization of spectral similarity networks, putative annotation of unknowns using MS2 data. | MetCirc | https://bioconductor.org/packages/release/bioc/html/MetCirc.html | [@naake_2017] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Annotation | Putative annotation of unknowns using MS2 data, clustering of MS2 data. | CluMSID | https://bioconductor.org/packages/devel/bioc/html/CluMSID.html | [@depke_2019] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Annotation | Putative annotation of unknowns using MS2 data. | compMS2Miner | https://github.com/WMBEdmands/compMS2Miner | [@William M B edmands_2017] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Generation of metabolite networks | Biochemical reaction networks, spectral and structural similarity networks. | MetaMapR | http://dgrapov.github.io/MetaMapR/ | [@grapov_2015] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Generation of metabolite networks | Correlation-based networks, structural similarity networks. | Metabox | http://kwanjeeraw.github.io/metabox/ | [@wanichthanarak_2017] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Generation of metabolite networks | Targeted metabolome-wide association studies. | MetabNet | https://sourceforge.net/projects/metabnet/ | [@uppal_2015] | SF | |
| Table 8: R packages for network analysis and Biochemical pathways. | Generation of metabolite networks | Generation of scale-free correlation-based networks. | WGCNA | https://cran.r-project.org/package=WGCNA | [@langfelder_2008] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Analysis of -omics data, pathway, transcription factor and target gene identification. | pwOmics | https://doi.org/doi:10.18129/B9.bioc.pwOmics | [@wachter_2015] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | MSEA a metabolite set enrichment analysis with factor loading in principal component analysis. | mseapca | https://cran.r-project.org/package=mseapca | [@yamamoto_2014] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Enrichment analysis of a list of affected metabolites. | tmod | https://cran.r-project.org/package=tmod | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Network-based enrichment analysis of a list of affected metabolites. | FELLA | https://bioconductor.org/packages/release/bioc/html/FELLA.html | [@picart-armada_2017] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Pathway-based enrichment analysis of a list of affected metabolites. | CePa | https://cran.r-project.org/package=CePa | [@gu_2013] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Differential analysis, modules/sub-pathway identification using networks. | MetaboDiff | https://github.com/andreasmock/MetaboDiff | [@mock_2018] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Integrates metabolic networks and RNA-seq data to construct condition-specific series of metabolic sub-networks and applies to gene set enrichment analysis | metaboGSE | https://cran.r-project.org/package=metaboGSE | [@tran_2018] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Differential analysis. | SDAMS | https://bioconductor.org/packages/release/bioc/html/SDAMS.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Biomarker identification. | lilikoi | https://cran.r-project.org/package=lilikoi | [@al-akwaa_2018] | CRAN | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Biomarker identification. | INDEED | https://bioconductor.org/packages/release/bioc/html/INDEED.html | [@zuo_2016] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Biomarker identification. | MoDentify | https://github.com/krumsieklab/MoDentify | [@do_2019] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Pathway activity profiling. | PAPi | https://doi.org/doi:10.18129/B9.bioc.PAPi | [@R. B. M. aggio_2010] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Pathway activity profiling. | pathwayPCA | https://www.bioconductor.org/packages/devel/bioc/html/pathwayPCA.html | [@odom_2019] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Flux balance analysis. | BiGGR | https://bioconductor.org/packages/release/bioc/html/BiGGR.html | [@gavai_2015] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Flux balance analysis. | abcdeFBA | https://cran.r-project.org/package=abcdeFBA | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Flux balance analysis. | sybil | https://cran.r-project.org/package=sybil | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Flux balance analysis. | fbar | https://cran.r-project.org/package=fbar | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Identification of affected pathway from phenotype data (interface with graphite). | SPIA | https://www.bioconductor.org/packages/release/bioc/html/SPIA.html | [@tarca_2009] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Identification of affected pathway from phenotype data (interface with graphite). | clipper | https://www.bioconductor.org/packages/release/bioc/html/clipper.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Interface to PathVisio and WikiPathways and pathway analysis and enrichment. | RPathVisio | https://github.com/PathVisio/RpathVisio | [@bohler_2015] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Enrichment analysis of a list of genes and metabolites. | RaMP | https://github.com/Mathelab/RaMP-DB | [@B. zhang_2018] | GitHub | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway analysis | Simulation of longitudinal metabolomics data based on an underlying biological network | MetaboLouise | https://cran.r-project.org/package=MetaboLouise | CRAN | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | BioPax parser and representation in R. | rBiopaxParser | https://www.bioconductor.org/packages/release/bioc/html/rBiopaxParser.html | [@kramer_2013] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to KEGG, Biocarta, Reactome, NCI/Nature Pathway Interaction Database, HumanCyc, Panther, SMPDB and PharmGKB. | graphite | http://bioconductor.org/packages/release/bioc/html/graphite.html | [@sales_2012, 2018] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to NCI Pathways Database. | NCIgraph | https://www.bioconductor.org/packages/release/bioc/html/NCIgraph.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to KEGG. | pathview | https://bioconductor.org/packages/release/bioc/html/pathview.html | [@luo_2013] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to KEGG. | KEGGgraph | https://www.bioconductor.org/packages/release/bioc/html/KEGGgraph.html | [@J. D. zhang_2009] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to systems biology markup language (SBML). | SBMLR | https://www.bioconductor.org/packages/release/bioc/html/SBMLR.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to systems biology markup language (SBML). | rsbml | https://bioconductor.org/packages/release/bioc/html/rsbml.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to Gaggle-enabled software (Cytoscape, Firegoose, Gaggle Genome browser). | gaggle | https://bioconductor.org/packages/release/bioc/html/gaggle.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to molecular interaction databases. | PSICQUIC | https://www.bioconductor.org/packages/release/bioc/html/PSICQUIC.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to KEGG REST server. | KEGGREST | http://bioconductor.org/packages/release/bioc/html/KEGGREST.html | BioC | ||
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to BioPAX OWL files and the Pathway Commons (PW) molecular interaction database. | paxtoolsr | http://bioconductor.org/packages/release/bioc/html/paxtoolsr.html | [@luna_2016] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Interface to WikiPathways. | rWikiPathways | https://bioconductor.org/packages/release/bioc/html/rWikiPathways.html | [@slenter_2018] | BioC | |
| Table 8: R packages for network analysis and Biochemical pathways. | Pathway resources and interfaces | Database that integrates metabolite and gene biological pathways from HMDB, KEGG, Reactome, and WikiPathways. Includes user-friendly R Shiny web application for queries and pathway enrichment analysis. | RaMP-DB | https://github.com/Mathelab/RaMP-DB/ | [@B. zhang_2018] | GitHub | |
| Table 9: R packages with multifunctional workflows. | Convenience wrapper for pre-processing tools (XCMS, CAMERA) and a number of statistical analyses. | MAIT | https://www.bioconductor.org/packages/release/bioc/html/MAIT.html | [@fernández-albert, llorach, andrés-lacueva_2014] | BioC | ||
| Table 9: R packages with multifunctional workflows. | Preprocessing (xcms), replicate merging, noise, blank and missingness filtering, feature grouping, annotation of known compounds, isotopic labeling analysis, annotation from KEGG or HMDB, common biotransformations and probabilistic putative metabolite annotation using MetAssign. | mzMatch | https://github.com/andzajan/mzmatch.R | [@daly_2014; @scheltema_2011] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | XCMS and CAMERA based workflow for non-targeted processing of LC-MS datasets, It includes pre-processing, peak picking,peak filtering, data normalization and descriptive statistics calculation. | MStractor | https://github.com/search?q=MStractor | GitHub | |||
| Table 9: R packages with multifunctional workflows. | Performs simultaneous raw data to mzXML conversion (MSConvert), peak-picking, automatic PCA outlier detection and statistical analysis, visualization and possible MS2 target list determination during an MS1 metabolomic profiling experiment. | simExTargId | https://github.com/WMBEdmands/simExTargId | [@William M. B. edmands_2017] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Pre-processing of large LC-MS datasets. Performs automatic PCA with iterative automatic outlier removal and, clustering analysis and biomarker discovery. | MetMSLine | https://github.com/WMBEdmands/MetMSLine | [@William M B edmands_2015] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Workflow for the systematic analysis of 1H NMR metabolomics dataset in quantitative genetics. Performs pre-processing, mQTL mapping, metabolites structural assignment and offers data visualisation tools. | mQTL.NMR | https://www.bioconductor.org/packages/3.5/bioc/html/mQTL.NMR.html | [@hedjazi_2015] | BioC | ||
| Table 9: R packages with multifunctional workflows. | Workflow for pre-processing, qc, annotation and statistical data analysis of LC-MS and GC-MS based metabolomics data to be submitted to public repositories. | MetaDB | https://github.com/rmylonas/MetaDB | [@franceschi_2014] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Specmine is a framework mainly built on a number of already published packages. It supports data processing form different analytical platforms (LC-MS, GC-MS, NMR, IR, UV-Vis). | specmine | https://github.com/cran/specmine | [@costa_2016] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Common interface for a number of different MS based data processing software. It covers various aspects, such as data preparation and data extraction, formula calculation, compound identification and reporting. | patRoon | https://github.com/rickhelmus/patRoon | GitHub | |||
| Table 9: R packages with multifunctional workflows. | Processing of high resolution of LC-MS data for environmental trend analysis. | enviMass | https://zenodo.org/record/1213098 | Zenodo | |||
| Table 9: R packages with multifunctional workflows. | Workflow for preprocessing of LC-HRMS data, suspect screening, screening for transformation products using combinatorial prediction, and interactive filtering based on ratios between sample groups. | RMassScreening | https://github.com/meowcat/RMassScreening | [@stravs_2017, 2019] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Workflow to perform pre-processing, statistical analysis and metabolite identifications based on database search of detected spectra. | MetaboNexus | https://github.com/tohweizhong/MetaboNexus | [@S.-M. huang_2014] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Shiny-based platform to extract differential features from LC-MS data, includes XCMS-based feature detection, statistical analysis, prediction of molecular formulas, annotation of MS2 spectra, MS2 molecular networking and chemical compound database search. | METABOseek | https://github.com/mjhelf/METABOseek | GitHub | |||
| Table 9: R packages with multifunctional workflows. | RShiny interface to Metabolomics packages & MetaboAnalyst scripts. | MetaboShiny | https://github.com/UMCUGenetics/MetaboShiny/ | [@wolthuis_2019] | GitHub | ||
| Table 9: R packages with multifunctional workflows. | Preprocessing and visualizing for LC-MS data, as well as statistical analyses, mainly based on univariate linear models. | amp | https://github.com/antonvsdata/amp | GitHub | |||
| Table 11: Packages to interface R with other languages and workflow environments | Given an R function and its manual page, make the documented function available in Galaxy. | RGalaxy | http://bioconductor.org/packages/release/bioc/html/RGalaxy.html | BioC | |||
| Table 11: Packages to interface R with other languages and workflow environments | Integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents. | Rcpp | https://cran.r-project.org/package=Rcpp | [@eddelbuettel_2011] | CRAN | ||
| Table 11: Packages to interface R with other languages and workflow environments | Low-Level R to Java Interface. | rJava | https://cran.r-project.org/package=rJava | CRAN | |||
| Table 11: Packages to interface R with other languages and workflow environments | Interface to 'Python' modules, classes, and functions and translation between R and Python objects. | reticulate | https://cran.r-project.org/package=reticulate | CRAN | |||
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | 12 HPLC-MS NetCDF files (Agilent 1100 LC-MSD SL). | faahKO | http://bioconductor.org/packages/release/data/experiment/html/faahKO.html | [@saghatelian_2004] | BioC | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | 16 UPLC-MS mzData files (Bruker microTOFq). | mtbls2 | http://bioconductor.org/packages/release/data/experiment/html/mtbls2.html | [@neumann_2013] | BioC | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | 12 UPLC-MS mzML files (AB Sciex TripleTOF 5600, SWATH mode). | mtbls297 | https://github.com/sneumann/mtbls297/ | [@balcke_2017] | GitHub | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | Different raw MS files (LTQ, TripleQ, FTICR, Orbitrap, QTOF) some in different formats (mzML, mzXML, mzData, mzData.gz, NetCDF, mz5). Also mzid format from proteomics. | msdata | http://bioconductor.org/packages/release/data/experiment/html/msdata.html | BioC | ||
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | Metadata and DDA MS/MS spectra of 15 narcotics standards (LTQ Orbitrap XL). | RMassBankData | https://bioconductor.org/packages/release/data/experiment/html/RMassBankData.html | [@stravs_2013] | BioC | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | 183 x 109 peak table. | ropls | http://bioconductor.org/packages/release/bioc/html/ropls.html | [@thévenot_2015] | BioC | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | 69 x 5,501 peak table. | biosigner | http://bioconductor.org/packages/release/bioc/html/biosigner.html | [@rinaudo_2016] | BioC | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | 40 x 1,632 peak table. | BioMark | https://cran.r-project.org/web/packages/BioMark/index.html | [@wehrens_2012] | CRAN | |
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | Raw MS files from a set of blanks and standards that contain common environmental contaminants (acquired with Bruker maXis 4G). | patRoonData | https://github.com/rickhelmus/patRoonData | GitHub | ||
| Table 12: Metabolomics data sets packaged as R packages. | LC-MS | Proteomics, metabolomics GC-MS and Lipidomics data from Calu-3 cell culture; 3 mockulum treated and 9 MERS-CoV treated; Time point, 18 hour from MassIVE dataset ids MSV000079152, MSV000079153, MSV000079154. | pmartRdata | https://github.com/pmartR/pmartRdata | GitHub | ||
| Table 12: Metabolomics data sets packaged as R packages. | FIA-MS | 6 mzML files (human plasma spiked with 40 compounds acquired in positive mode on an orbitrap fusion). | plasFIA | http://bioconductor.org/packages/release/data/experiment/html/plasFIA.html | BioC | ||
| Table 12: Metabolomics data sets packaged as R packages. | FIA-MS | mzML files (Thermo Exactive) from comparison of leaf tissue from 4 B. distachyon ecotypes with Flow-infusion electrospray ionisation-high resolution mass spectrometry (FIE-HRMS). Also includes data sets with 10 technical injections of human urine and another 10 injections from leaf tissue (ecotype ABR1). | metaboData | https://github.com/aberHRML/metaboData | GitHub | ||
| Table 12: Metabolomics data sets packaged as R packages. | GC-MS | 52 x 154 peak table. | pcaMethods | http://bioconductor.org/packages/release/bioc/html/pcaMethods.html | [@stacklies_2007] | BioC | |
| Table 12: Metabolomics data sets packaged as R packages. | NMR | 18 x 189 peak table. | MetabolAnalyze | https://cran.r-project.org/package=MetabolAnalyze | CRAN | ||
| Table 12: Metabolomics data sets packaged as R packages. | NMR | 33 x 164 peak table. | MetabolAnalyze | https://cran.r-project.org/package=MetabolAnalyze | CRAN | ||
| Table 12: Metabolomics data sets packaged as R packages. | NMR | ASICSdata: 1D NMR spectra for ASICS. | ASICSdata | https://www.bioconductor.org/packages/release/data/experiment/vignettes/ASICSdata/inst/doc/ASICSdata.html | [@lefort_2019] | BioC | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Subset of functions from the Chemistry Development Kit. Provide a computer readable representation of molecular structures and provide functions to import structures from different molecule structure description formats, manipulate structures, visualize structures and calculate properties and molecular fingerprints. | rcdk | https://cran.r-project.org/package=rcdk | [@guha_2007] | CRAN | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Similar torcdkin functionality and provides more fingerprints and clustering methods and provides additional tools through querying the ChemMine Tools web service. | ChemmineR | https://doi.org/doi:10.18129/B9.bioc.ChemmineR | [@Y. cao_2008] | BioC | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Provides conversion of structure representation through OpenBabel. | ChemmineOB | https://doi.org/doi:10.18129/B9.bioc.ChemmineOB | BioC | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Exposes functionalities of the RDKit library, including reading and writing of SF files and calculating a few physicochemical properties. | RRDKit | https://github.com/pauca/RRDKit | GitHub | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Read and write InChI and InChIKey from and torcdk. | rinchi | https://github.com/rajarshi/cdkr | GitHub | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Maximum Common Substructure Searching using ChemmineR structures. | FmcsR | https://doi.org/doi:10.18129/B9.bioc.fmcsR | [@Yan wang_2013] | BioC | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Basic cheminformatics functions tailored for mass spectrometry applications, enhancing functionality available in other packages likercdk, enviPat, RMassBank etc. | RChemMass | https://github.com/schymane/RChemMass | GitHub | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Structure representation and manipulation | Provides fingerprinting methods forrcdk. | fingerprint | https://cran.r-project.org/package=fingerprint | CRAN | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Calculation of molecular properties. | camb | https://github.com/cambDI/camb | [@murrell_2015] | GitHub | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Querying information from PubChem. | Rpubchem | https://cran.r-project.org/package=rpubchem | CRAN | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Querying information from various web services (CACTUS, CTS, PubChem, ChemSpider) as part of compound list generation. | RMassBank | https://doi.org/doi:10.18129/B9.bioc.RMassBank | [@stravs_2013] | BioC | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Querying information from a large number of databases. | webchem | https://cran.r-project.org/package=webchem | [@szöcs_2015] | CRAN | |
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | R Interface to the ClassyFire REST API. | classyfireR | https://cran.r-project.org/web/packages/classyfireR/index.html | CRAN | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Allows mapping of identifiers from one database to another, for metabolites, genes, proteins, and interactions. | BridgeDbR | https://doi.org/10.18129/B9.bioc.BridgeDbR | BioC | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations. | hmdbQuery | https://doi.org/doi:10.18129/B9.bioc.hmdbQuery | BioC | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Parsers for many compound databases including HMDB, MetaCyc, ChEBI, FooDB, Wikidata, WikiPathways, RIKEN respect, MaConDa, T3DB, KEGG, Drugbank, LipidMaps, MetaboLights, Phenol-Explorer, MassBank. | MetaDBparse | https://github.com/UMCUGenetics/MetaDBparse | GitHub | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Functionality to create and use compound databases generated from (mostly publicly) available resources such as HMDB, ChEBI and PubChem. | CompoundDb | https://github.com/EuracBiomedicalResearch/CompoundDb | GitHub | ||
| Table 7: R Packages for molecule structures and chemical structure databases. | Database queries | Standardized and extensible framework to query chemical and biological databases. | biodb | https://github.com/pkrog/biodb | GitHub | ||
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Preprocessing (xcms), replicate merging, noise, blank and missingness filtering, feature grouping, annotation of known compounds, isotopic labeling analysis, annotation from KEGG or HMDB, common biotransformations and probabilistic putative metabolite annotation using MetAssign. | mzMatch | https://github.com/andzajan/mzmatch.R | [@daly_2014; @scheltema_2011] | GitHub | |
| Table 2: R packages for ion species grouping, annotation, molecular formula generation and accurate mass lookup. | Ion/adduct/fragment annotation | Putative annotation of unknowns in MS1 data. | xMSAnnotator | https://sourceforge.net/projects/xmsannotator/ | [@uppal_2017] | SF | |
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | Integration of omics data using multivariate methods such as PLS. Performs community detection and network analysis to allow visualization of positive or negative associations between different datasets generated using samples from the same individuals. Also available as ashinyapp (https://kuppal.shinyapps.io/xmwas). | xMWAS | https://github.com/kuppal2/xMWAS | [@uppal_2018] | GitHub | |
| Table 6: R packages for statistical analysis of metabolomics data. | Omics Data integration | Joint metabolic model-based analysis of metabolomics measurements and taxonomic composition from microbial communities. | MIMOSA | https://github.com/borenstein-lab/MIMOSA/tree/master/mimosa | [@noecker_2016] | GitHub |
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