scholarly journals RCy3: Network biology using Cytoscape from within R

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1774 ◽  
Author(s):  
Julia A. Gustavsen ◽  
Shraddha Pai ◽  
Ruth Isserlin ◽  
Barry Demchak ◽  
Alexander R. Pico
Keyword(s):  
Shortest Path ◽  
Future Development ◽  
Enrichment Analysis ◽  
Network Biology ◽  
R Package ◽  
Programming Environment ◽  
R Packages ◽  
R Programming ◽  
Shortest Path Algorithms ◽  
Rest Api

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.

scholarly journals RCy3: Network biology using Cytoscape from within R

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1774 ◽  
Author(s):  
Julia A. Gustavsen ◽  
Shraddha Pai ◽  
Ruth Isserlin ◽  
Barry Demchak ◽  
Alexander R. Pico
Keyword(s):  
Shortest Path ◽  
Future Development ◽  
Enrichment Analysis ◽  
Network Biology ◽  
R Package ◽  
Programming Environment ◽  
R Packages ◽  
R Programming ◽  
Shortest Path Algorithms ◽  
Rest Api

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.

scholarly journals RCy3: Network Biology using Cytoscape from within R

2019 ◽  
Author(s):  
Julia A. Gustavsen ◽  
Shraddha Pai ◽  
Ruth Isserlin ◽  
Barry Demchak ◽  
Alexander R. Pico
Keyword(s):  
Shortest Path ◽  
Future Development ◽  
Enrichment Analysis ◽  
Network Biology ◽  
R Package ◽  
Programming Environment ◽  
R Packages ◽  
R Programming ◽  
Shortest Path Algorithms ◽  
Rest Api

AbstractRCy3 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 igraph and 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.

scholarly journals RCy3: Network biology using Cytoscape from within R

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1774
Author(s):  
Julia A. Gustavsen ◽  
Shraddha Pai ◽  
Ruth Isserlin ◽  
Barry Demchak ◽  
Alexander R. Pico
Keyword(s):  
Shortest Path ◽  
Future Development ◽  
Enrichment Analysis ◽  
Network Biology ◽  
R Package ◽  
Programming Environment ◽  
R Packages ◽  
R Programming ◽  
Shortest Path Algorithms ◽  
Rest Api

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.

scholarly journals gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 709 ◽  
Author(s):  
Liis Kolberg ◽  
Uku Raudvere ◽  
Ivan Kuzmin ◽  
Jaak Vilo ◽  
Hedi Peterson
Keyword(s):  
Gene List ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Automated Analysis ◽  
R Package ◽  
Biological Data ◽  
Functional Enrichment ◽  
Link Type ◽  
Functional Profiling ◽  
Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

scholarly journals see: An R Package for Visualizing Statistical Models

2021 ◽  
Author(s):  
Daniel Lüdecke ◽  
Indrajeet Patil ◽  
Mattan S. Ben-Shachar ◽  
Brenton M. Wiernik ◽  
Philip Waggoner ◽  
...  
Keyword(s):  
Programming Language ◽  
Statistical Models ◽  
R Package ◽  
Model Parameters ◽  
R Programming Language ◽  
R Packages ◽  
R Programming ◽  
And Performance ◽  
Performance Diagnostics ◽  
Scientific Reporting

The see package is embedded in the easystats ecosystem, a collection of R packages that operate in synergy to provide a consistent and intuitive syntax when working with statistical models in the R programming language (R Core Team, 2021). Most easystats packages return comprehensive numeric summaries of model parameters and performance. The see package complements these numeric summaries with a host of functions and tools to produce a range of publication-ready visualizations for model parameters, predictions, and performance diagnostics. As a core pillar of easystats, the see package helps users to utilize visualization for more informative, communicable, and well-rounded scientific reporting.

scholarly journals InterMineR: an R package for InterMine databases

2019 ◽  
Vol 35 (17) ◽  
pp. 3206-3207 ◽  
Author(s):  
Konstantinos A Kyritsis ◽  
Bing Wang ◽  
Julie Sullivan ◽  
Rachel Lyne ◽  
Gos Micklem
Keyword(s):  
Data Retrieval ◽  
R Package ◽  
Supplementary Information ◽  
Programming Environment ◽  
Biological Databases ◽  
Statistical Tools ◽  
Bioconductor Project ◽  
Term Enrichment ◽  
R Programming ◽  
Downstream Analysis

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.

scholarly journals webMCP-counter: a web interface for transcriptomics-based quantification of immune and stromal cells in heterogeneous human or murine samples

2020 ◽  
Author(s):  
Maxime Meylan ◽  
Etienne Becht ◽  
Catherine Sautès-Fridman ◽  
Aurélien de Reyniès ◽  
Wolf H. Fridman ◽  
...  
Keyword(s):  
Stromal Cells ◽  
R Package ◽  
Web Interface ◽  
Transcriptomic Data ◽  
R Programming Language ◽  
Link Type ◽  
Precise Estimation ◽  
R Packages ◽  
R Programming ◽  
User Friendly

AbstractSummaryWe previously reported MCP-counter and mMCP-counter, methods that allow precise estimation of the immune and stromal composition of human and murine samples from bulk transcriptomic data, but they were only distributed as R packages. Here, we report webMCP-counter, a user-friendly web interface to allow all users to use these methods, regardless of their proficiency in the R programming language.Availability and ImplementationFreely available from http://134.157.229.105:3838/webMCP/. Website developed with the R package shiny. Source code available from GitHub: https://github.com/FPetitprez/webMCP-counter.

scholarly journals compGeometeR: an R package for computational geometry

2021 ◽  
Author(s):  
Thomas R Etherington ◽  
O. Pascal Omondiagbe
Keyword(s):  
Computational Geometry ◽  
Data Structures ◽  
Future Development ◽  
R Package ◽  
Scientific Workflows ◽  
Algorithms And Data Structures ◽  
Domain Specific ◽  
R Packages

Computational geometry algorithms and data structures are widely applied across numerous scientific domains, and there a variety of R packages that implement computational geometry functionality. However, these packages often work in specific numbers of dimensions, do not have directly compatible data structures, and include additional non-computational geometry functionality that can be domain specific. Our objective in developing the compGeometeR package is to implement in a generic and consistent framework the most commonly used combinatorial computational geometry algorithms so that they can be easily combined and integrated into domain specific scientific workflows. We briefly explain the discrete and digital combinatorial computational geometry algorithms available in compGeometeR, and identify priorities for future development.

scholarly journals gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 709 ◽  
Author(s):  
Liis Kolberg ◽  
Uku Raudvere ◽  
Ivan Kuzmin ◽  
Jaak Vilo ◽  
Hedi Peterson
Keyword(s):  
Gene List ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Automated Analysis ◽  
R Package ◽  
Biological Data ◽  
Functional Enrichment ◽  
Link Type ◽  
Functional Profiling ◽  
Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

scholarly journals Lilikoi V2.0: a deep-learning enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data

2020 ◽  
Author(s):  
Xinying Fang ◽  
Yu Liu ◽  
Zhijie Ren ◽  
Yuheng Du ◽  
Qianhui Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
R Package ◽  
Programming Environment ◽  
Learning Method ◽  
Metabolomics Data ◽  
Machine Learning Methods ◽  
Diagnosis And Prognosis ◽  
R Programming ◽  
Significant Addition ◽  
Metabolomics Analysis

ABSTRACTPreviously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2.0 R package has implemented a deep-learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-PH model and the deep-learning based Cox-nnet model. Additionally, Lilikoi v2.0 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression. In summary, Lilikoi v2.0 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

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