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. 2023 Aug 16;18(8):e0285339. doi: 10.1371/journal.pone.0285339

cyjShiny: A cytoscape.js R Shiny Widget for network visualization and analysis

Augustin Luna 1,2,*, Omar Shah 3, Chris Sander 1,2, Paul Shannon 4
Editor: Yanbin Yin5
PMCID: PMC10431631  PMID: 37585474

Abstract

cyjShiny is an open-source R package that allows users to embed network visualization into Shiny apps and R Markdown documents. cyjShiny (https://github.com/cytoscape/cyjShiny) builds on the cytoscape.js Javascript graph library. Additionally, the package provides helper functions to convert common R data representations (e.g., data.frame) into forms compatible with cytoscape.js.

Introduction

Interactions in various forms (e.g., regulatory, metabolic, neuronal, ecological interaction networks) are fundamental to our understanding of biological phenomena. Further, researchers are increasingly consuming scientific information through web-based data portals. R Shiny (shiny.rstudio.com) is a framework that simplifies the development of interactive web applications for research projects using the R programming language. The Shiny framework for web development is extensible using the htmlwidgets R library (htmlwidgets.org) that allows the use of Javascript-based visualization libraries in R. Here we present the cyjShiny R package, a wrapper package for the cytoscape.js. cytoscape.js. is a widely used Javascript library for network analysis and visualization that is extensible through plugins [1].

The use case for cyjShiny is differentiated from either cytoscape.js and Cytoscape Desktop [1,2]. In comparison to cytoscape.js, cyjShiny simplifies the effort needed (by users who may be familiar with R but unfamiliar with Javascript) to embed interactive network visualizations into either R Markdown-based reports or R Shiny-based web applications. While Cytoscape Desktop is a ready-to-use platform for end-users to visualize and analyze their data, cyjShiny is a building block for developers to integrate into their existing databases, tools, or workflows; see the Use Case section for examples.

Methods

Implementation

cyjShiny is implemented in the R programming language; the R package is compatible with versions of R 3.5 and above.

Data Formats: cytoscape.js requires a JSON-based input data structure [1]. To facilitate the creation of this data structure, cyjShiny provides two helper functions graphNELtoJSON() and dataFramesToJSON(). First, the graphNELtoJSON() function allows users to convert network data that they may have in the graphNEL format from the graph R package. Users of the igraph R package can convert to the graphNEL representation using the as_graphnel() method found in igraph. Second, the dataFramesToJSON() function can convert a data.frame to the necessary JSON representation. This function takes the parameter, tbl.edges, which should have three columns source, edge, and interaction; source and target are identifiers for nodes, while interaction provides a category for the interaction in the form of a string. If users wish to include node attributes (e.g., values or categories to be used for node styling), users may provide a second optional data frame: tbl.nodes. The tbl.nodes data.frame must include an "id" column to be mapped to the source or target columns of the tbl.edges data.frame. The result of either of these functions is then input to the cyjShiny() method which will render the network.

Network Styling: Additionally, to the network data, users must provide styling information. cytoscape.js uses a styling format heavily inspired by Cascading Style Sheets (CSS) to provide this functionality. All of a user’s style choices, simple to complex, must be encoded into a style file, the path to which may be passed to cyjShiny when an instance is constructed, or subsequently in an R function call, loadStyleFile(“yourStyle.js”), from within the Shiny application. This means that users can, for instance, set the node property "border-color" to "black" to have nodes with black borders consistent with how one would style a webpage table. More information regarding cytoscape.js styling is available here: js.cytoscape.org/#style.

Plugins: Cytoscape.js provides an application programming interface (API) that allows the development of plugins to extend its functionality. These plugins provide functionality not present in the core library. Currently, there are over 60 user-contributed extensions (i.e., plugins) for cytoscape.js (js.cytoscape.org/#extensions). cyjShiny pre-packages several plugins to provide various network layouts. For example, two of the included algorithms are fCoSE (Fast Compound Spring Embedder) layout and the Klay algorithms [3]. These two algorithms are suitable for compound networks; a biological example of a compound network is a network that includes protein complexes and there is interest in keeping predefined complexes as a unit.

Use cases

Shiny Application: We provide several demos as part of the package (github.com/cytoscape/cyjShiny/tree/master/inst/demos). Fig 1 shows the demo (github.com/cytoscape/cyjShiny/blob/master/inst/demos/basicDemo/app.R) using a sample yeast protein-protein interaction network. This demo covers many key features that users are likely to want. Including basic requirements of showing the network within a Shiny app, but also, more advanced features such as 1) switching between different data (to color nodes under different conditions) and stylings on the same network, 2) highlighting nodes, 3) fitting the network to the available screen space, and 4) animating networks by looping through dataset visualized on the network. The demo is available online at https://cyjshiny.shinyapps.io/basicDemo. The project website README provides direct links to various demos including a minimal quick start example to more complex demos that 1) show how a network can utilize pre-generated layouts and 2) utilize networks generated via Cytoscape Desktop, as well as others; links on the README will be updated as demos are added or removed. We further provide documentation to explain how networks on cyjShiny can be styled to suit developer needs.

Fig 1. Screenshot of a demo Shiny application with a sample yeast protein-protein interaction network.

Fig 1

Open in a new tab

Menus shown on the left-hand sidebar allow users to control various properties of the network including the layout and the data overlaid on nodes.

R Markdown: While cyjShiny was developed for inclusion in Shiny applications, cyjShiny can be embedded in R Markdown-based scientific notebooks. Such notebooks provide a report for a given analysis that presents code alongside its outputs (e.g., plots, tables), as well as prose text explaining the code and output. An example notebook has also been provided in the source code that embeds a network visualization widget.

Examples: Many biological databases utilize cytoscape.js for network visualization of molecular interactions and co-expression networks across a wide range of topics, including FlyBase, FerrDb, IntAct, MolluscDB, DEPOD, ATTED-II, and Pathway Commons [4–10]. Likewise, developers of gene set analysis methodologies, such as g:Profiler and WebGestalt, have utilized cytoscape.js to visualize both interaction networks as well as Gene Ontology hierarchies [11,12]. Using such databases and tools researchers can interactively explore datasets and results via web applications [13,14]. By utilizing cyjShiny developers can both develop methodologies and web applications utilizing only R code thereby lowering the barrier to creating such web-based interactive tools. This strategy has been employed by the developers of BayesNetBP that utilize R Shiny for the exploration of results to their method analyzing Bayesian networks [15].

Conclusion

cyjShiny facilitates the integration of interactive network visualizations into Shiny apps and R Markdown documents using only the R programming language. A tutorial (vignette) and demo applications have been provided as part of the package for users to learn the customization options available. These interactive visualizations enrich the exploration of user datasets and can be customized to address the needs of specific projects.

Acknowledgments

We thank the developers of cytoscape.js and the maintainers of the R CRAN repository. Additionally, we would like to thank the coordinators of the Google Summer of Code program.

Data Availability

The package is available from the CRAN R repository: cran.r-project.org/web/packages/cyjShiny/index.html. Documentation and source code for cyjShiny is available at github.com/cytoscape/cyjShiny under the MIT license.

Funding Statement

The authors received funding from the Google Summer of Code program and funding for the National Resource for Network Biology (NRNB) from the National Institute of General Medical Sciences (NIGMS P41 GM103504). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Yanbin Yin

23 Feb 2023

PONE-D-22-24150

cyjShiny: A cytoscape.js R Shiny Widget for Network Visualization and Analysis

PLOS ONE

Dear Dr. Luna,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Additional Editor Comments:

​Sorry for being late. Reviews came in delayed due to the winter break. Please consider reviewer #4's comment to include detailed tutorial to make the tool more user friendly.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Partly

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Reviewer #1: N/A

Reviewer #2: N/A

Reviewer #3: N/A

Reviewer #4: N/A

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors present cyjShiny, a tool in R for visualising networks with Shiny. cyjShiny provides a mechanism for interactively exploring network data in a web browser, as generated from analyses (e.g. bioinfo.) in R. This software package makes exploration and demonstration of network visualisations accessible to researchers who are familiar with R notebooks -- but who may not be so familiar with other programming environments, such as web technologies.

The use of cyjShiny promises to ease the interpretation and dissemination of network data in research, and so it is my opinion that this article is suitable for publication.

I have no suggestions re. revisions.

Reviewer #2: The manuscript is well-written. The R package is easy to install. The authors have also provided several demos on their GitHub page with the complete codes available. My only concern is that the current R package ShinyApp) is not very user-friendly to users with no or limited R knowledge. I would suggest adding more details to the user manual and allowing the user to upload and visualize the data immediately after launching the ShinyApp without writing too much code.

Reviewer #3: The authors propose a web portal called cyjShiny for visualizing network data. This application is built using cytoscape.js and provides APIs for converting graph data from dataframe format in R or iGraph format into JSON format. While the authors do not present many novel innovations in cytoscape.js, this tool is still useful for researchers who use R to write Rmarkdown or develop websites.

There are a few areas that need improvement:

1. The README.md and WIKI in the GitHub repository should be carefully rewritten. For example, in https://github.com/cytoscape/cyjShiny/wiki/installation, the script tinyApp.R is mentioned, but it is not present in the repository.

2. It would be helpful if an online portal was provided to showcase demos of the tool.

Reviewer #4: The authors presented an R package cyjShiny that allows users to embed network visualization into Shiny apps and R Markdown documents. I installed the R package on Windows 10 and tested the several demos provided by the authors. The demos and functionalities work smoothly. My concern is that the help tutorials and the manuscript is too short and not user-friendly at all. The authors should spend more words and paragraphs to improve the manuscript. For example, how can the R package cyjShiny be used in scientific studies? What’s the relationship between cyjShiny, and other tools including cytoscape.js and cytoscape? The help manual for cyjShiny is too simple. I can not find a helpful tutorial on how to use this R package. If you do not dig into the code, you will not be aware the existence of 10 demos provided by the authors.

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PLoS One. 2023 Aug 16;18(8):e0285339. doi: 10.1371/journal.pone.0285339.r002

Author response to Decision Letter 0

3 Apr 2023

The included resubmission cover letter describes specific changes to the text in response to reviewer feedback.

Decision Letter 1

Yanbin Yin

20 Apr 2023

cyjShiny: A cytoscape.js R Shiny Widget for Network Visualization and Analysis

PONE-D-22-24150R1

Dear Dr. Luna,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Yanbin Yin

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PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: No

Reviewer #4: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: (No Response)

Reviewer #4: (No Response)

**********

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Reviewer #3: No

Reviewer #4: Yes: Wen Yao

**********

Acceptance letter

Yanbin Yin

3 May 2023

PONE-D-22-24150R1

cyjShiny: A cytoscape.js R Shiny Widget for Network Visualization and Analysis

Dear Dr. Luna:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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PLOS ONE

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