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Journal articles on the topic 'R packages'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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1

Gonçalves, Emanuel, and Julio Saez-Rodriguez. "Cyrface: An interface from Cytoscape to R that provides a user interface to R packages." F1000Research 2 (September 19, 2013): 192. http://dx.doi.org/10.12688/f1000research.2-192.v1.

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There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape plug-in termed Cyrface that allows Cytoscape plug-ins to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it links the R packages with the capabilities of Cytoscape and its plug-ins, in particular network visualization and analysis. Cyrface’s utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface homepage (http://www.ebi.ac.uk/saezrodriguez/cyrface/).
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2

Gonçalves, Emanuel, Franz Mirlach, and Julio Saez-Rodriguez. "Cyrface: An interface from Cytoscape to R that provides a user interface to R packages." F1000Research 2 (July 1, 2014): 192. http://dx.doi.org/10.12688/f1000research.2-192.v2.

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There is an increasing number of software packages to analyse biological experimental data in the R environment. In particular, Bioconductor, a repository of curated R packages, is one of the most comprehensive resources for bioinformatics and biostatistics. The use of these packages is increasing, but it requires a basic understanding of the R language, as well as the syntax of the specific package used. The availability of user graphical interfaces for these packages would decrease the learning curve and broaden their application. Here, we present a Cytoscape app termed Cyrface that allows Cytoscape apps to connect to any function and package developed in R. Cyrface can be used to run R packages from within the Cytoscape environment making use of a graphical user interface. Moreover, it can link R packages with the capabilities of Cytoscape and its apps, in particular network visualization and analysis. Cyrface’s utility has been demonstrated for two Bioconductor packages (CellNOptR and DrugVsDisease), and here we further illustrate its usage by implementing a workflow of data analysis and visualization. Download links, installation instructions and user guides can be accessed from the Cyrface’s homepage (http://www.ebi.ac.uk/saezrodriguez/cyrface/) and from the Cytoscape app store (http://apps.cytoscape.org/apps/cyrface).
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3

Schumacker, Randall. "Psychometric Packages in R." Measurement: Interdisciplinary Research and Perspectives 17, no. 2 (April 3, 2019): 106–12. http://dx.doi.org/10.1080/15366367.2018.1544434.

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4

Polanin, Joshua R., Emily A. Hennessy, and Emily E. Tanner-Smith. "A Review of Meta-Analysis Packages in R." Journal of Educational and Behavioral Statistics 42, no. 2 (November 30, 2016): 206–42. http://dx.doi.org/10.3102/1076998616674315.

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Meta-analysis is a statistical technique that allows an analyst to synthesize effect sizes from multiple primary studies. To estimate meta-analysis models, the open-source statistical environment R is quickly becoming a popular choice. The meta-analytic community has contributed to this growth by developing numerous packages specific to meta-analysis. The purpose of this study is to locate all publicly available meta-analytic R packages. We located 63 packages via a comprehensive online search. To help elucidate these functionalities to the field, we describe each of the packages, recommend applications for researchers interested in using R for meta-analyses, provide a brief tutorial of two meta-analysis packages, and make suggestions for future meta-analytic R package creators.
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5

Hraber, Peter. "Language games: Advanced R & R packages." Complexity 21, S2 (March 23, 2016): 635–40. http://dx.doi.org/10.1002/cplx.21780.

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6

Victor Oribamise, B., and Lauren L. Hulsman Hanna. "37 Sibs: an R toolkit for computation of relatedness measures using large pedigrees." Journal of Animal Science 98, Supplement_3 (November 2, 2020): 41–42. http://dx.doi.org/10.1093/jas/skaa054.074.

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Abstract Without appropriate relationships present in a given population, identifying dominance effects in the expression of desirable traits is challenging. Including non-additive effects is desirable to increase accuracy of breeding values. There is no current user-friendly tool package to investigate genetic relatedness in large pedigrees. The objective was to develop and implement efficient algorithms in R to calculate and visualize measures of relatedness (e.g., sibling and family structure, numerator relationship matrices) for large pedigrees. Comparisons to current R packages (Table 1) are also made. Functions to assign animals to families, summary of sibling counts, calculation of numerator relationship matrix (NRM), and NRM summary by groups were created, providing a comprehensive toolkit (Sibs package) not found in other packages. Pedigrees of various sizes (n = 20, 4,035, 120,000 and 132,833) were used to test functionality and compare to current packages. All runs were conducted on a Windows-based computer with an 8 GB RAM, 2.5 GHz Intel Core i7 processor. Other packages had no significant difference in runtime when constructing the NRM for small pedigrees (n = 20) compared to Sibs (0 to 0.05 s difference). However, packages such as ggroups, AGHmatrix, and pedigree were 10 to 15 min slower than Sibs for a 4,035-individual pedigree. Packages nadiv and pedigreemm competed with Sibs (0.30 to 60 s slower than Sibs), but no package besides Sibs was able to complete the 132,833-individual pedigree due to memory allocation issues in R. The nadiv package was closest with a pedigree of 120,000 individuals, but took 37 min to complete (13 min slower than Sibs). This package also provides easier input of pedigrees and is more encompassing of such relatedness measures than other packages (Table 1). Furthermore, it can provide an option to utilize other packages such as GCA for connectedness calculations when using large pedigrees.
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7

Astagneau, Paul C., Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven. "Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective." Hydrology and Earth System Sciences 25, no. 7 (July 8, 2021): 3937–73. http://dx.doi.org/10.5194/hess-25-3937-2021.

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Abstract. Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall–runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R.
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8

Mowinckel, Athanasia M., and Didac Vidal-Piñeiro. "Visualization of Brain Statistics With R Packages ggseg and ggseg3d." Advances in Methods and Practices in Psychological Science 3, no. 4 (November 30, 2020): 466–83. http://dx.doi.org/10.1177/2515245920928009.

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There is an increased emphasis on visualizing neuroimaging results in more intuitive ways. Common statistical tools for dissemination of these results, such as bar charts, lack the spatial dimension that is inherent in neuroimaging data. Here we present two packages for the statistical software R that integrate this spatial component. The ggseg and ggseg3d packages visualize predefined brain segmentations as 2D polygons and 3D meshes, respectively. Both packages are integrated with other well-established R packages, which allows great flexibility. In this Tutorial, we describe the main data and functions in the ggseg and ggseg3d packages for visualization of brain atlases. The highlighted functions are able to display brain-segmentation plots in R. Further, the accompanying ggsegExtra package includes a wider collection of atlases and is intended for community-based efforts to develop additional compatible atlases for ggseg and ggseg3d. Overall, the ggseg packages facilitate parcellation-based visualizations in R, improve and facilitate the dissemination of results, and increase the efficiency of workflows.
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9

Kyritsis, Konstantinos A., Bing Wang, Julie Sullivan, Rachel Lyne, and Gos Micklem. "InterMineR: an R package for InterMine databases." Bioinformatics 35, no. 17 (January 22, 2019): 3206–7. http://dx.doi.org/10.1093/bioinformatics/btz039.

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Abstract Summary InterMineR is a package designed to provide a flexible interface between the R programming environment and biological databases built using the InterMine platform. The package offers access to the flexible query builder and the library of term enrichment tools of the InterMine framework, as well as interoperability with other Bioconductor packages. This facilitates automation of data retrieval tasks as well as downstream analysis with existing statistical tools in the R environment. Availability and implementation InterMineR is free and open source, released under the LGPL licence and available from the Bioconductor project and Github (https://bioconductor.org/packages/release/bioc/html/InterMineR.html, https://github.com/intermine/interMineR). Supplementary information Supplementary data are available at Bioinformatics online.
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10

Plummer, Martyn. "Portable C++ for R Packages." R Journal 3, no. 2 (2011): 60. http://dx.doi.org/10.32614/rj-2011-020.

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11

Hengstberger-Sims, Cecily, and Margaret A. McMillan. "Problem-based learning packages: considerations for neophyte package writers." Nurse Education Today 13, no. 1 (February 1993): 73–77. http://dx.doi.org/10.1016/0260-6917(93)90013-r.

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12

Li, Yan, Matthew Sperrin, and Tjeerd van Staa. "R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R." F1000Research 8 (December 23, 2019): 2139. http://dx.doi.org/10.12688/f1000research.21679.1.

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Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to better understand risk prediction scores and improve future risk prediction models. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.
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Li, Yan, Matthew Sperrin, and Tjeerd van Staa. "R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R." F1000Research 8 (February 28, 2020): 2139. http://dx.doi.org/10.12688/f1000research.21679.2.

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Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to better understand risk prediction scores and improve future risk prediction models. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.
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14

Li, Yan, Matthew Sperrin, and Tjeerd van Staa. "R package “QRISK3”: an unofficial research purposed implementation of ClinRisk’s QRISK3 algorithm into R." F1000Research 8 (May 22, 2020): 2139. http://dx.doi.org/10.12688/f1000research.21679.3.

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Cardiovascular disease has been the leading cause of death for decades. Risk prediction models are used to identify high risk patients; the most common model used in the UK is ClinRisk’s QRISK3. In this paper we describe the implementation of the QRISK3 algorithm into an R package. The package was successfully validated by the open sourced QRISK3 algorithm and QRISK3 SAS program. We provide detailed examples of the use of the package, including assigning QRISK3 scores for a large cohort of patients. This R package could help the research community to improve future risk prediction models based on a currently used risk prediction model. The package is available from CRAN: https://cran.r-project.org/web/packages/QRISK3/index.html.
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15

Olson, Nathan D., Nidhi Shah, Jayaram Kancherla, Justin Wagner, Joseph N. Paulson, and Hector Corrada Bravo. "metagenomeFeatures: an R package for working with 16S rRNA reference databases and marker-gene survey feature data." Bioinformatics 35, no. 19 (March 1, 2019): 3870–72. http://dx.doi.org/10.1093/bioinformatics/btz136.

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Abstract Summary We developed the metagenomeFeatures R Bioconductor package along with annotation packages for three 16S rRNA databases (Greengenes, RDP and SILVA) to facilitate working with 16S rRNA databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different databases facilitating database comparison and exploration. The mgFeatures-class represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R. Availability and implementation https://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html. Supplementary information Supplementary material is available at Bioinformatics online.
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16

Boettiger, Carl. "Generating CodeMeta Metadata for R Packages." Journal of Open Source Software 2, no. 19 (November 13, 2017): 454. http://dx.doi.org/10.21105/joss.00454.

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17

Li, Kai, and Shenmeng Xu. "Measuring the impact of R packages." Proceedings of the Association for Information Science and Technology 54, no. 1 (January 2017): 739–41. http://dx.doi.org/10.1002/pra2.2017.14505401138.

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18

Rupp, André A., and Peter W. van Rijn. "GDINA and CDM Packages in R." Measurement: Interdisciplinary Research and Perspectives 16, no. 1 (January 2, 2018): 71–77. http://dx.doi.org/10.1080/15366367.2018.1437243.

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19

Sen, Sedat, and Ragip Terzi. "A Comparison of Software Packages Available for DINA Model Estimation." Applied Psychological Measurement 44, no. 2 (April 23, 2019): 150–64. http://dx.doi.org/10.1177/0146621619843822.

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This article provides a review of software packages for fitting maximum likelihood estimation of the deterministic input, noisy “and” gate (DINA) model. Six software packages—flexMIRT, Latent GOLD, mdltm, Mplus, OxEdit, and R—are considered. Each package is reviewed regarding data manipulation, statistical capabilities, output, and documentation. The results of these packages are compared using a sample data set and a Q-matrix. The article aims to give the reader a summary of the different capabilities of each package.
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20

Kaleb, Klara, Alex Warwick Vesztrocy, Adrian Altenhoff, and Christophe Dessimoz. "Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the OmaDB packages for R and Python." F1000Research 8 (January 10, 2019): 42. http://dx.doi.org/10.12688/f1000research.17548.1.

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The Orthologous Matrix (OMA) is a well-established resource to identify orthologs among many genomes. Here, we present two recent additions to its programmatic interface, namely a REST API, and user-friendly R and Python packages called OmaDB. These should further facilitate the incorporation of OMA data into computational scripts and pipelines. The REST API can be freely accessed at https://omabrowser.org/api. The R OmaDB package is available as part of Bioconductor at http://bioconductor.org/packages/OmaDB/, and the omadb Python package is available from the Python Package Index (PyPI) at https://pypi.org/project/omadb/.
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Kaleb, Klara, Alex Warwick Vesztrocy, Adrian Altenhoff, and Christophe Dessimoz. "Expanding the Orthologous Matrix (OMA) programmatic interfaces: REST API and the OmaDB packages for R and Python." F1000Research 8 (March 29, 2019): 42. http://dx.doi.org/10.12688/f1000research.17548.2.

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The Orthologous Matrix (OMA) is a well-established resource to identify orthologs among many genomes. Here, we present two recent additions to its programmatic interface, namely a REST API, and user-friendly R and Python packages called OmaDB. These should further facilitate the incorporation of OMA data into computational scripts and pipelines. The REST API can be freely accessed at https://omabrowser.org/api. The R OmaDB package is available as part of Bioconductor at http://bioconductor.org/packages/OmaDB/, and the omadb Python package is available from the Python Package Index (PyPI) at https://pypi.org/project/omadb/.
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22

Borcherding, Nicholas, Nicholas L. Bormann, and Gloria Kraus. "scRepertoire: An R-based toolkit for single-cell immune receptor analysis." F1000Research 9 (June 15, 2020): 47. http://dx.doi.org/10.12688/f1000research.22139.2.

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Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with a number of popular R packages for single-cell expression, such as Seurat. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.
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23

Mair, Patrick, Eva Hofmann, Kathrin Gruber, Reinhold Hatzinger, Achim Zeileis, and Kurt Hornik. "Motivation, values, and work design as drivers of participation in the R open source project for statistical computing." Proceedings of the National Academy of Sciences 112, no. 48 (November 9, 2015): 14788–92. http://dx.doi.org/10.1073/pnas.1506047112.

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One of the cornerstones of the R system for statistical computing is the multitude of packages contributed by numerous package authors. This amount of packages makes an extremely broad range of statistical techniques and other quantitative methods freely available. Thus far, no empirical study has investigated psychological factors that drive authors to participate in the R project. This article presents a study of R package authors, collecting data on different types of participation (number of packages, participation in mailing lists, participation in conferences), three psychological scales (types of motivation, psychological values, and work design characteristics), and various socio-demographic factors. The data are analyzed using item response models and subsequent generalized linear models, showing that the most important determinants for participation are a hybrid form of motivation and the social characteristics of the work design. Other factors are found to have less impact or influence only specific aspects of participation.
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24

Li, Pumin, Qi Xu, Xu Hua, Zhongwei Xie, Jie Li, and Jin Wang. "primirTSS: an R package for identifying cell-specific microRNA transcription start sites." Bioinformatics 36, no. 11 (March 14, 2020): 3605–6. http://dx.doi.org/10.1093/bioinformatics/btaa173.

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Abstract Summary The R/Bioconductor package primirTSS is a fast and convenient tool that allows implementation of the analytical method to identify transcription start sites of microRNAs by integrating ChIP-seq data of H3K4me3 and Pol II. It further ensures the precision by employing the conservation score and sequence features. The tool showed a good performance when using H3K4me3 or Pol II Chip-seq data alone as input, which brings convenience to applications where multiple datasets are hard to acquire. This flexible package is provided with both R-programming interfaces as well as graphical web interfaces. Availability and implementation primirTSS is available at: http://bioconductor.org/packages/primirTSS. The documentation of the package including an accompanying tutorial was deposited at: https://bioconductor.org/packages/release/bioc/vignettes/primirTSS/inst/doc/primirTSS.html. Contact jwang@nju.edu.cn Supplementary information Supplementary data are available at Bioinformatics online.
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25

Borcherding, Nicholas, and Nicholas L. Bormann. "scRepertoire: An R-based toolkit for single-cell immune receptor analysis." F1000Research 9 (January 27, 2020): 47. http://dx.doi.org/10.12688/f1000research.22139.1.

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Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.
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26

Funk, Michael A. "DNA barcodes in small packages." Science 368, no. 6495 (June 4, 2020): 1076.18–1078. http://dx.doi.org/10.1126/science.368.6495.1076-r.

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27

Bartłomowicz, Tomasz. "COMPARATIVE ANALYSIS OF ANALYTIC HIERARCHY PROCESS R PACKAGES." Informatyka Ekonomiczna 3, no. 41 (2016): 9–18. http://dx.doi.org/10.15611/ie.2016.3.01.

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28

Russell, Pamela H., and Debashis Ghosh. "Radtools: R utilities for smooth navigation of medical image data." F1000Research 7 (December 24, 2018): 1976. http://dx.doi.org/10.12688/f1000research.17139.1.

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The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice. The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files. We present radtools, an R package for smooth navigation of medical image data. Radtools makes the problem of extracting image metadata trivially simple, providing simple functions to explore and return information in familiar R data structures. Radtools also facilitates extraction of image data and viewing of image slices. The package is freely available under the MIT license at https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network (https://cran.r-project.org/package=radtools).
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Russell, Pamela H., and Debashis Ghosh. "Radtools: R utilities for convenient extraction of medical image metadata." F1000Research 7 (January 25, 2019): 1976. http://dx.doi.org/10.12688/f1000research.17139.2.

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The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice. The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files. We present radtools, an R package for convenient extraction of medical image metadata. Radtools provides simple functions to explore and return metadata in familiar R data structures. For convenience, radtools also includes wrappers of existing tools for extraction of pixel data and viewing of image slices. The package is freely available under the MIT license at https://github.com/pamelarussell/radtools and is easily installable from the Comprehensive R Archive Network (https://cran.r-project.org/package=radtools).
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Graves, Spencer, Sundar Dorai-Raj, and Romain François. "sos: Searching Help Pages of R Packages." R Journal 1, no. 2 (2009): 56. http://dx.doi.org/10.32614/rj-2009-017.

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31

Joo, Rocío, Matthew E. Boone, Thomas A. Clay, Samantha C. Patrick, Susana Clusella‐Trullas, and Mathieu Basille. "Navigating through the r packages for movement." Journal of Animal Ecology 89, no. 1 (October 28, 2019): 248–67. http://dx.doi.org/10.1111/1365-2656.13116.

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32

Rainer, Johannes, Laurent Gatto, and Christian X. Weichenberger. "ensembldb: an R package to create and use Ensembl-based annotation resources." Bioinformatics 35, no. 17 (January 25, 2019): 3151–53. http://dx.doi.org/10.1093/bioinformatics/btz031.

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Abstract Summary Bioinformatics research frequently involves handling gene-centric data such as exons, transcripts, proteins and their positions relative to a reference coordinate system. The ensembldb Bioconductor package retrieves and stores Ensembl-based genetic annotations and positional information, and furthermore offers identifier conversion and coordinates mappings for gene-associated data. In support of reproducible research, data are tied to Ensembl releases and are kept separately from the software. Premade data packages are available for a variety of genomes and Ensembl releases. Three examples demonstrate typical use cases of this software. Availability and implementation ensembldb is part of Bioconductor (https://bioconductor.org/packages/ensembldb). Supplementary information Supplementary data are available at Bioinformatics online.
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33

Russell, Pamela H., and Debashis Ghosh. "Radtools: R utilities for convenient extraction of medical image metadata." F1000Research 7 (March 25, 2019): 1976. http://dx.doi.org/10.12688/f1000research.17139.3.

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The radiology community has adopted several widely used standards for medical image files, including the popular DICOM (Digital Imaging and Communication in Medicine) and NIfTI (Neuroimaging Informatics Technology Initiative) standards. These file formats include image intensities as well as potentially extensive metadata. The NIfTI standard specifies a particular set of header fields describing the image and minimal information about the scan. DICOM headers can include any of >4,000 available metadata attributes spanning a variety of topics. NIfTI files contain all slices for an image series, while DICOM files capture single slices and image series are typically organized into a directory. Each DICOM file contains metadata for the image series as well as the individual image slice. The programming environment R is popular for data analysis due to its free and open code, active ecosystem of tools and users, and excellent system of contributed packages. Currently, many published radiological image analyses are performed with proprietary software or custom unpublished scripts. However, R is increasing in popularity in this area due to several packages for processing and analysis of image files. While these R packages handle image import and processing, no existing package makes image metadata conveniently accessible. Extracting image metadata, combining across slices, and converting to useful formats can be prohibitively cumbersome, especially for DICOM files. We present radtools, an R package for convenient extraction of medical image metadata. Radtools provides simple functions to explore and return metadata in familiar R data structures. For convenience, radtools also includes wrappers of existing tools for extraction of pixel data and viewing of image slices. The package is freely available under the MIT license at GitHub and is easily installable from the Comprehensive R Archive Network.
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Gustavsen, Julia A., Shraddha Pai, Ruth Isserlin, Barry Demchak, and Alexander R. Pico. "RCy3: Network biology using Cytoscape from within R." F1000Research 8 (October 18, 2019): 1774. http://dx.doi.org/10.12688/f1000research.20887.1.

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RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.
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Gustavsen, Julia A., Shraddha Pai, Ruth Isserlin, Barry Demchak, and Alexander R. Pico. "RCy3: Network biology using Cytoscape from within R." F1000Research 8 (November 27, 2019): 1774. http://dx.doi.org/10.12688/f1000research.20887.2.

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RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.
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Gustavsen, Julia A., Shraddha Pai, Ruth Isserlin, Barry Demchak, and Alexander R. Pico. "RCy3: Network biology using Cytoscape from within R." F1000Research 8 (December 4, 2019): 1774. http://dx.doi.org/10.12688/f1000research.20887.3.

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RCy3 is an R package in Bioconductor that communicates with Cytoscape via its REST API, providing access to the full feature set of Cytoscape from within the R programming environment. RCy3 has been redesigned to streamline its usage and future development as part of a broader Cytoscape Automation effort. Over 100 new functions have been added, including dozens of helper functions specifically for intuitive data overlay operations. Over 40 Cytoscape apps have implemented automation support so far, making hundreds of additional operations accessible via RCy3. Two-way conversion with networks from \textit{igraph} and \textit{graph} ensures interoperability with existing network biology workflows and dozens of other Bioconductor packages. These capabilities are demonstrated in a series of use cases involving public databases, enrichment analysis pipelines, shortest path algorithms and more. With RCy3, bioinformaticians will be able to quickly deliver reproducible network biology workflows as integrations of Cytoscape functions, complex custom analyses and other R packages.
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37

Love, Michael I., Charlotte Soneson, and Rob Patro. "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification." F1000Research 7 (June 27, 2018): 952. http://dx.doi.org/10.12688/f1000research.15398.1.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
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Love, Michael I., Charlotte Soneson, and Rob Patro. "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification." F1000Research 7 (September 14, 2018): 952. http://dx.doi.org/10.12688/f1000research.15398.2.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
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Love, Michael I., Charlotte Soneson, and Rob Patro. "Swimming downstream: statistical analysis of differential transcript usage following Salmon quantification." F1000Research 7 (October 1, 2018): 952. http://dx.doi.org/10.12688/f1000research.15398.3.

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Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Here we present a simple workflow using a set of existing R/Bioconductor packages for analysis of DTU. We show how these packages can be used downstream of RNA-seq quantification using the Salmon software package. The entire pipeline is fast, benefiting from inference steps by Salmon to quantify expression at the transcript level. The workflow includes live, runnable code chunks for analysis using DRIMSeq and DEXSeq, as well as for performing two-stage testing of DTU using the stageR package, a statistical framework to screen at the gene level and then confirm which transcripts within the significant genes show evidence of DTU. We evaluate these packages and other related packages on a simulated dataset with parameters estimated from real data.
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40

Russell, Seth, Tellen D. Bennett, and Debashis Ghosh. "Software engineering principles to improve quality and performance of R software." PeerJ Computer Science 5 (February 4, 2019): e175. http://dx.doi.org/10.7717/peerj-cs.175.

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Today’s computational researchers are expected to be highly proficient in using software to solve a wide range of problems ranging from processing large datasets to developing personalized treatment strategies from a growing range of options. Researchers are well versed in their own field, but may lack formal training and appropriate mentorship in software engineering principles. Two major themes not covered in most university coursework nor current literature are software testing and software optimization. Through a survey of all currently available Comprehensive R Archive Network packages, we show that reproducible and replicable software tests are frequently not available and that many packages do not appear to employ software performance and optimization tools and techniques. Through use of examples from an existing R package, we demonstrate powerful testing and optimization techniques that can improve the quality of any researcher’s software.
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41

Foster, Zachary S. L., Scott Chamberlain, and Niklaus J. Grünwald. "Taxa: An R package implementing data standards and methods for taxonomic data." F1000Research 7 (March 5, 2018): 272. http://dx.doi.org/10.12688/f1000research.14013.1.

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The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.
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42

Foster, Zachary S. L., Scott Chamberlain, and Niklaus J. Grünwald. "Taxa: An R package implementing data standards and methods for taxonomic data." F1000Research 7 (September 11, 2018): 272. http://dx.doi.org/10.12688/f1000research.14013.2.

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The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.
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43

Tsagris, Michail, and Ioannis Tsamardinos. "Feature selection with the R package MXM." F1000Research 7 (September 30, 2019): 1505. http://dx.doi.org/10.12688/f1000research.16216.2.

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Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications.
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44

Chávez, Joselyn, Carmina Barberena-Jonas, Jesus E. Sotelo-Fonseca, José Alquicira-Hernández, Heladia Salgado, Leonardo Collado-Torres, and Alejandro Reyes. "Programmatic access to bacterial regulatory networks with regutools." Bioinformatics 36, no. 16 (June 23, 2020): 4532–34. http://dx.doi.org/10.1093/bioinformatics/btaa575.

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Abstract Summary RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and implementation regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.
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45

Chamberlain, Scott A., and Eduard Szöcs. "taxize: taxonomic search and retrieval in R." F1000Research 2 (September 18, 2013): 191. http://dx.doi.org/10.12688/f1000research.2-191.v1.

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All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that’s easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.
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46

Chamberlain, Scott A., and Eduard Szöcs. "taxize: taxonomic search and retrieval in R." F1000Research 2 (October 28, 2013): 191. http://dx.doi.org/10.12688/f1000research.2-191.v2.

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All species are hierarchically related to one another, and we use taxonomic names to label the nodes in this hierarchy. Taxonomic data is becoming increasingly available on the web, but scientists need a way to access it in a programmatic fashion that’s easy and reproducible. We have developed taxize, an open-source software package (freely available from http://cran.r-project.org/web/packages/taxize/index.html) for the R language. taxize provides simple, programmatic access to taxonomic data for 13 data sources around the web. We discuss the need for a taxonomic toolbelt in R, and outline a suite of use cases for which taxize is ideally suited (including a full workflow as an appendix). The taxize package facilitates open and reproducible science by allowing taxonomic data collection to be done in the open-source R platform.
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47

Sugiyama, Mahito, M. Elisabetta Ghisu, Felipe Llinares-López, and Karsten Borgwardt. "graphkernels: R and Python packages for graph comparison." Bioinformatics 34, no. 3 (September 22, 2017): 530–32. http://dx.doi.org/10.1093/bioinformatics/btx602.

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48

pynam, Venkateswarlu, Kolli srikanth, Ashok Surgala, and Aravind Bammidi. "Introduction to Text Mining with R using packages." International Journal of Computer Trends and Technology 54, no. 2 (December 25, 2017): 116–19. http://dx.doi.org/10.14445/22312803/ijctt-v54p118.

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49

Somasundaram, Eashwar,V, Shael,E Brown, Adam Litzler, Jacob,G Scott, and Raoul,R Wadhwa. "Benchmarking R packages for Calculation of Persistent Homology." R Journal 13, no. 1 (2021): 184. http://dx.doi.org/10.32614/rj-2021-033.

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50

Kort, Eric J., and Stefan Jovinge. "Streamlined analysis of LINCS L1000 data with the slinky package for R." Bioinformatics 35, no. 17 (January 10, 2019): 3176–77. http://dx.doi.org/10.1093/bioinformatics/btz002.

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Abstract Summary The L1000 dataset from the NIH LINCS program holds the promise to deconvolute a wide range of biological questions in transcriptional space. However, using this large and decentralized dataset presents its own challenges. The slinky package was created to streamline the process of identifying samples of interest and their corresponding control samples, and loading their associated expression data and metadata. The package can integrate with workflows leveraging the BioConductor collection of tools by encapsulating the L1000 data as a SummarizedExperiment object. Availability and implementation Slinky is freely available as an R package at http://bioconductor.org/packages/slinky
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