dataesr/scientific-paragraphs-categorization huggingface dataset:
Another advantage is that, in most cases, workflows allow a certain degree of flexibility so that functionalities can be used as standalone functions (modular workflows) to better comply with the user's needs. The packages covering larger parts of metabolomics workflows available in R are listed in Table  9. These multifunctional packages include comprehensive workflows that focus on multiple aspects, such as data pre-processing, data validation, preliminary statistical analysis and data visualisation of large metabolomics datasets. The considered workflows support both MS-based data (LC-MS and GC-MS) and data generated by different analytical platforms. MAIT (Metabolite Automatic Identification Toolkit) offers pre-processing, annotation, statistical analysis and data visualisation. It relies on xcms for peak picking and on CAMERA for the preliminary annotation.
In addition to CAMERA, the peak annotation process is implemented by including a functionality that allows relating in-source mass losses to specific biotransformations. Human biotransformations are already included, additional biotransformation criteria can be added by the end user. MAIT also provides several statistical tools and visual representations (e. g. , PCA, boxplot, PLS), as well as a function to perform identifications using accurate mass search in HMDB. MetMSLine shows some similarities with MAIT in terms of processing stages (xcms-based pre-processing, multivariate statistics, metabolite identifications). Functionalities characterising MetMSLine include normalisation, signal drift correction using a smoothing method, noise transformation and outlier removal. SimExTargId is a wrapper of different software and R packages for LC-MS data.
It includes tools for data conversion (Proteowizard), peak picking and annotation (xcms and CAMERA), outlier detection and data correction (MetMSLine), and basic statistical analysis. A special feature of SimeExTargId is the real time monitoring of the different workflow stages aimed at metabolomics core facilities; users are notified by email in case of processing errors (e. g. , outlier detection, signal drift). mzMatch is slightly different from the above-mentioned workflows and is designed to fit in a broader processing pipeline itself. The project also includes a dedicated file format (peakML) and a Java environment. The different modules can still be used independently. mzMatch supports peak picking and grouping using xcms, reproducibility calculation, data normalisation. The peakMonitor app identifies peaks using the local database.
The identification is performed on the basis of m/z and retention time values with user-defined mass accuracy and retention time deviation values. MetaDB is built by integrating the metaMS R package into a web application written in Grails. It has also been designed to be integrated with the MetaboLights database. MetaDB supports both LC-MS and GC-MS datasets and offers a wide range of functionalities, including: data storage and metadata management (using the ISA-Tab format and ISACreator tool  [300, 301] ), peak picking and annotation (via metaMS, an xcms and CAMERA add-on) and QC plots. MStractor is designed for non-expert users to carry out non-targeted data processing on LC-MS experiments. It gathers xcms and CAMERA functions in a user-friendly pipeline, requiring minimal input and providing graphical QC outputs throughout the workflow.
specmine provides a general framework that addresses a variety of different analytical platforms, such as LC-MS, GC-MS, NMR, IR and UV-Vis. The package supports many data formats and includes the possibility of adding metadata in a tabular format. It relies on xcms for LC-MS and GC-MS data pre-processing, on hyperSpec for NMR, IR and UV/VIS data processing and on MAIT for metabolite identification. specmine provides scripts for missing values imputation, univariate and multivariate statistics and machine learning methods. Several case studies are available for testing purposes. mQTL. NMR is a package specifically for the systematic analysis of 1H NMR metabolomics in quantitative genetics. The package mainly focuses on NMR spectral data pre-processing (normalisation, scaling and peak alignment), mQTL mapping in different model organisms, structural assignment of marker metabolites, and result visualisation.
enviMass is a comprehensive workflow for the data-mining of LC-MS and GC-MS datasets, which also supports MS/MS experiments. It provides the user with a graphical user interface (GUI) and a flexible workflow structure covering common processing steps such as data conversion, peak picking, noise removal,-mass re-calibration, data normalisation, and blank subtraction. It also offers several more specific and advanced functionalities, including isotopologue and adduct grouping, homologous series detection and visualisation, estimation of atom counts for nontarget components, temporal sequences, profile trend detection and processing of both data dependent and data independent acquisition of MS/MS experiments. RMassScreening is a workflow for batch processing of LC-HRMS datasets using a script interface, YAML-based setting configuration and visual interactive data evaluation.
It provides wrappers for script-based usage of enviPick and basic enviMass components, and implements suspect screening and combinatorial prediction of possible metabolites (transformation products) from parent compounds. A GUI provides facilities to analyse the results, grouped by sample groups and experimental timepoints, by applying freely adjustable filters. MetaboNexus is an interactive data analysis platform for metabolomics experiments, which provides a user friendly R shiny-based GUI designed to work without the need for web server connections. It allows pre-processing (using xcms and MZmine), data scaling, univariate and multivariate statistics (t-test, ANOVA, PCA, PLS-DA, Random Forest, heatmap), putative metabolite identification (library matching of MS and MS/MS adduct with METLIN, HMDB and MassBank databases), and several functions for data visualisation.
User Interfaces and Workflow Management Systems Visualisation is an important part of data analysis. Traditionally, graphics in R have been focussed on creating static plots, while typical explorative studies generally require interactive visualisation to fully investigate the data. User interactions could range from simply zooming in chromatographic or spectroscopic data through to temporarily excluding data from a complex plot for clarity. Several packages in R are available for making interactive plots, e. g. , the Plotly library [311], to create interactive graphics from the static plots generated by the popular plotting framework ggplot2  [312]. The use of interactive plots in R is growing, and is helped by an increasing number of code examples. Another way interactive plots, and even full GUI tools, are being introduced into R is through the shiny framework, which can create web apps using the full power of R packages as the backend.
All of these environments can be run locally or installed on a (local or cloud-based) server. Recently, several initiatives have started to provide publicly available computing resources. Examples are e. g. , the previously mentioned rdrr. io, which offers to paste R code into an online console for execution. The console can also be embedded into individual websites. The same project also hosts rnotebook. io, which allows to create and run R notebooks. The shinyapps. io platform operated by RStudio Inc has free and paid options to host shiny apps. The binder project (involving members from large academic institutions and companies (like UC Berkeley, Cal Poly San Luis Obispo, Wild Tree Tech Switzerland, Netflix or Simula Research Lab) is an infrastructure for creating and using shareable, interactive and reproducible data analysis (not only) with R  [313]  by taking any GitHub repository, turning it into a Docker image and launching it on a cloud service.
The package holepunch  [314]  simplifies preparing an R project for launching on binder. A public instance is the mybinder. org service providing (limited) resources to execute R-based scripts in a hosted Rstudio, Jupyter notebook or applications written with e. g. , shiny. The binder infrastructure code is available on GitHub, so that the service can be offered by universities and research groups to its users, lifting the resource limitations of the public instance. In some cases, an R package can provide bindings to existing tools and libraries written in other languages (see Table  11 ). This is, for example, the case for the packages rcdk or MetFragR using the rJava bindings, or mzR, which is a wrapper around the Proteowizard C++ library using the Rcpp package. The fairly new reticulate package provides the corresponding infrastructure to execute Python from R code.
Metabolomics Data Sets Sharing of data has become increasingly common, and metabolomics data are available from MetaboLights  [326]  in the EU, GNPS  [122]  and Metabolomics Workbench  [327]  in the US. In the context of this review, we focus instead on data in R packages, which is important for development, unit testing, documentation and user training (see Table  12 ). While there is no difference in R between software and data packages per se, they are handled differently in the Bioconductor infrastructure and separate views exist. There are several data sets with raw data from LC-MS and flow injection analysis, which can be used by the data pre-processing packages in the previous sections. Other packages contain pre-processed data from GC-MS, LC-MS or NMR in the form of peak tables, which are then typically used in statistics packages, network analysis and other downstream analyses.
Conclusions This review surveyed both the scientific literature and the R landscape for packages relevant to metabolomics research. While it was very easy to find relevant packages in CRAN and even more so in BioC, many packages are scattered across other source code hosting platforms. While GitHub has a concept of topics (see github. com/search?q=topic:metabolomics+topic:r), and crawlers like rdrr. io can find R packages across several platforms, the best findability can be achieved through well-integrated umbrella projects like Bioconductor, which provide additional infrastructure and also improve the community interaction through conferences and workshops. This also shows the need for more detailed metadata of the R packages allowing easier mixing and matching of packages, noting that Bioconductor already does a very good job.
R packages already have a long-standing history of metadata annotation via their DESCRIPTION and CITATION files. These provide links to other packages (e. g. , dependencies and suggestions) and literature describing the package. Exposing package and vignette meta data with semantic approaches will support the community in developing further, more advanced multi-functional workflows for metabolomics. The authors have recently adopted Bioschemas  [330]  to make metadata more easily findable. For example, efforts to start annotation in vignettes allows the ELIXIR Training eSupport System TeSS (tess. oerc. ox. ac. uk) to pick up newer versions (see this git commit  [331] ), and efforts are underway to expose content from the DESCRIPTION file as Bioschemas annotations on Bioconductor (see this pull request  [332] ).
Acknowledgments: We thank all package developers for their contributions to a vibrant metaRbolomics community, and all participants of several workshops (International Conference of the  Metabolomics Society 2016, Dublin, Ireland and Dagstuhl 2017, Germany , and metaRbolomics 2019, Wittenberg, Germany), where R packages were discussed. For comments and advice in several sections we acknowledge  René Meier  from  IPB Halle  and  Natoiya Lloyd  from  Metabolomics Australia  (South Australian node) which is funded through  Bioplatforms Australia Pty Ltd. (BPA) , a  National Collaborative Research Infrastructure Strategy (NCRIS) , and investment from the  South Australian State Government  and the  Australian Wine Research Institute (AWRI). CB acknowledged funding by  US NIH  ( 1U01CA235507-01 ) and the  CSU Office of the Vice President for Research.
NH acknowledges funding by the  Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen , the  Regierende Bürgermeister von Berlin-inkl. Wissenschaft und Forschung  and from  BMBF  funding under grant number  031L0108A. EM acknowledges funding by  US NIH  ( 1R03CA222428-01 ). TN acknowledges the support by the  IMPRS-PMPG program  at the  MPI-MP. LN acknowledges "Metabolomics South Australia which is funded through  Bioplatforms Australia Pty Ltd. (BPA) , a  National Collaborative Research Infrastructure Strategy (NCRIS) , and investment from the  South Australian State Government and The Australian Wine Research Institute ". ELS is supported by the  Luxembourg National Research Fund (FNR)  for project  12341006. MS acknowledges funding from  Eawag. ET  acknowledges funding from the  ANR  ( MetaboHUB national infrastructure for metabolomics and fluxomics ,  ANR-11-INBS-0010 ).
peakANOVA [92] Combination of data from positive and negative ionisation mode finding common molecular entities. MScombine [93] CRAN InterpretMSSpectrum [97] CRAN Automated annotation using MS/MS data or databases and retention time. Calculation of spectral compMS2Miner [98] GitHub and chemical networks. Screening, annotation, and putative identification of mass spectral features in lipidomics. Default LOBSTAHS [99] BioC databases contain ~25,000 compounds. Automated annotation of fragments from MS and MS/MS and putative identification against simulated library fragments of ~500,000 lipid species across ~60 LipidMatch [100] GitHub lipid types. Annotation of lipid type and acyl groups on independent acquisition-mass spectrometry lipidomics based on fragmentation and LipidMS [101] CRAN intensity rules.
Automated quantification of metabolites by targeting mass spectral/retention time libraries into full baitmet [113] CRAN scan-acquired GC-MS chromatograms. MS/MS spectra similarity and unsupervised statistical methods. Workflow from raw data to CluMSID [114] BioC visualisations and is interfaceable with xcms. Import of spectra from different file formats such as NIST msp, mgf (mascot generic format), and library MSnio GitHub (Bruker) to MSnbase objects. Multi-purpose mass spectrometry package. Contains many different functions e. g. , isotope pattern calculation, spectrum similarity, chromatogram OrgMassSpecR CRAN plotting, reading of msp files and peptide related functions. Annotation of LC-MS data based on a database of fragments. MetaboList [115] CRAN In Silico Fragmentation In silico fragmentation of candidate structures.
Fit models include InDScal, PARAFAC, PARAFAC2, SCA, Tucker. Predictive multivariate modelling using PLS and Random Forest Data. Repeated double cross MUVR [179] GitLab unbiased validation and variable selection. Probabilistic PLS-DA, Random Forest, SVM, GBM, GLMNET, PAM models for spectral data. OmicsMarkeR [176] BioC Performs the O2PLS data integration method for two datasets yielding joint and data-specific parts for OmicsPLS [205] CRAN each dataset. Package for performing Partial Least Squares regression (PLS). pls [206] CRAN Variable selection methods for PLS, including significance multivariate correlation, selectivity ratio, variable importance in projections (VIP), loading plsVarSel [207] CRAN weights, and regression coefficients. It contains also some other modelling methods. Decompose a tensor of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations.
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 IntLIM [212] GitHub accessible as a user-friendly interface at intlim. bmi. osumc. edu. Statistical framework supporting many different types of multivariate analyses, e. g. , PCA, CCA, (sparse)PLS(-DA). mixOmics ggmixOmics [213] [163] CRAN Multi-omics base classes integrable with commonly used R Bioconductor objects for omics data; MultiDataSet [214] BioC container that holds omics results. Multiple co-inertia analysis of omics datasets (MCIA) is a multivariate approach for visualisation and integration of multi-omics datasets. The MCIA method is not dependent on feature annotation omicade4 [215] BioC therefore it can extract important features even when they are not present across all datasets.
Visualisation and imputation of missing values. VIM provides methods for the evaluation and visualisation of the type and patterns of missing data. The included imputation approaches are kNN, VIM [221] CRAN Hot-Deck, iterative robust model-based imputation, fast matching/imputation based on categorical variables and regression imputation. GUI for VIM. VIMGUI CRAN kNN-based imputation for microarray data. impute [222] BioC Bootstrap-based algorithm and diagnostics for fast and robust multiple imputation for cross sectional, time series or combined cross sectional and time Amelia [223] CRAN series data. Algorithms and diagnostics for the univariate imputation of time series data. imputeTS [224] CRAN 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, missMDA [225] CRAN a multiple correspondence analysis (MCA) model, a multiple factor analysis (MFA) model or factorial analysis for mixed data.
Random forest-based missing data imputation for mixed-type, nonparametric data. An out-of-bag missForest [226] CRAN (OOB) error estimate is used for model optimisation. Multivariate imputation by chained equations using fully conditional specifications for categorical, continuous and binary datasets. It includes various mice [227] CRAN diagnostic plots for the evaluation of the imputation quality. Missing data imputation using an approximate Bayesian framework. Diagnostic algorithms are included to analyse the models, the assumptions of mi [228] CRAN the imputation algorithm and the multiply imputed datasets. Iterative Gibbs sampler-based left-censored missing value imputation. GSimp [229] GitHub Multiple Workflow Steps Missing value imputation, filtering, normalisation and averaging of technical replications.
RaMP [292] GitHub Simulation of longitudinal metabolomics data based on an underlying biological network MetaboLouise CRAN Pathway Resources and Interfaces BioPax parser and representation in R. rBiopaxParser [293] BioC Interface to KEGG, Biocarta, Reactome, NCI/Nature Pathway Interaction Database, HumanCyc, Panther, graphite [294,295] BioC SMPDB and PharmGKB. Interface to NCI Pathways Database. NCIgraph BioC Interface to KEGG. pathview [296] BioC Interface to KEGG. KEGGgraph [297] BioC Interface to systems biology markup language (SBML). SBMLR BioC Interface to systems biology markup language (SBML). rsbml BioC Interface to Gaggle-enabled software (Cytoscape, Firegoose, Gaggle Genome browser). gaggle BioC Interface to molecular interaction databases. PSICQUIC BioC Interface to KEGG REST server.
KEGGREST BioC Interface to BioPAX OWL files and the Pathway Commons (PW) molecular interaction database. paxtoolsr [298] BioC Interface to WikiPathways. rWikiPathways [299] BioC Database that integrates metabolite and gene biological pathways from HMDB, KEGG, Reactome, and WikiPathways. Includes user-friendly R shiny RaMP-DB [292] GitHub web application for queries and pathway enrichment analysis.
Processing of high resolution of LC-MS data for environmental trend analysis. enviMass Zenodo Workflow for pre-processing of LC-HRMS data, suspect screening, screening for transformation products using combinatorial prediction, and RMassScreening [307,308] GitHub interactive filtering based on ratios between sample groups. Workflow to perform pre-processing, statistical analysis and metabolite identifications based on MetaboNexus [309] GitHub database search of detected spectra. shiny-based platform to extract differential features from LC-MS data, includes xcms-based feature detection, statistical analysis, prediction of molecular formulas, annotation of MS/MS spectra, MS/MS METABOseek GitHub molecular networking and chemical compound database search. shiny interface to Metabolomics packages and MetaboAnalyst scripts.
However, measurements of ice thickness are sparse, timely and expensive: the global Glacier Thickness Database  [5]  contains direct measurements for only about 4700 of the 215,000 inventoried glaciers  [6]. As part of the Ice Thickness Models Intercomparison eXperiment (ITMIX), several modeling methods were tested to map ice thickness distribution of mountain glaciers. This experiment and the related studies have shown that the accuracy of the estimates depends strongly on the quality of the input data, typically the surface flow velocity  [7] [8] [9] [10]. Hence, large scale, comprehensive and frequent (e. g. , sub-seasonal) mappings of the glacier surface velocity would be needed to accurately estimate ice thickness, but also to understand the variability in glacier dynamics. Nowadays, these speed maps are spatially and temporally scarce and at coarse resolution, which has hampered our understanding of glacier evolution in mountain regions  [11, 12].
Data and Study Regions We present hereafter glacier surface velocity maps for three different mountain ranges in different climatic regions:  (1)  the southern Alps of New Zealand in Oceania with a temperate maritime climate; (2) the Cordillera Blanca in the tropical Andes; and (3) the European Alps which have a wide variety of climatic influences from the Mediterranean to continental  [17]. The southern Alps of New Zealand have large (up to tens of km 2 ) and fast-flowing mountain glaciers, while the glaciers of the Cordillera Blanca rarely exceed 2 km in length and many of them have a highly crevassed surface due to steep slopes. The glaciers of the European Alps are larger than the Andean glaciers but smaller than the New Zealand glaciers, with both steep and flat slopes. For all these regions, we estimate glacier surface velocity fields at the regional scale using Sentinel-2 data with a resolution of 10 m for the years 2017 and 2018.
Description of the Workflow The workflow consists of four modules: (1) database creation and image preparation; (2) glacier surface displacement calculation; (3) data calibration and geo-database creation; and (4) post-processing with data filtering and averaging to obtain glacier surface velocity maps. All codes are written entirely in open-source programming languages: Python 3. 7 and Fortran. The processing chain is designed for massively parallel processing with SQL (Structured Query Language).
Database Initialization and Image Search First, a shapefile with the contour of the study area is generated from the Randolph Glacier Inventory v6. 0  [6, 19]. This contour includes not only glaciers but a whole mountain range in order to preserve sufficiently stable ground surface outside the glaciers to be able to calibration while still limiting the size of the area to be treated, hence reducing the computation time. For each region, an SQL database is initialized in which is stored a list of all existing images and associated metadata for the studied region. Existing Sentinel-2 and Landsat 7/8 images are searched using the API from ESA and USGS, respectively. When the images are then paired to map the displacement, another database is initialized. This second SQL database is then fully integrated into the rest of the workflow and used to monitor and update the status of the processing step for each individual image pair.