Network Visualization core logic

[pnavada]

PR Type

enhancement

---

Description

• Implemented core logic for network visualization using Cytoscape.js in the server module.
• Developed a UI module to support network visualization, including data upload and settings.
• Integrated the network server module into the main server function.
• Added a new tab for network interpretation in the UI.

---

Changes walkthrough 📝

Relevant files

Enhancement

module-visualize-network-server.R Implement network visualization server logic with Cytoscape.js R/module-visualize-network-server.R Added a server module to handle network visualization. Implemented reactive expressions for data processing and network rendering. Integrated Cytoscape.js for network visualization. Added error handling for CSV data upload. +114/-0  module-visualize-network-ui.R Develop UI for network visualization with Cytoscape.js      R/module-visualize-network-ui.R Created a UI module for network visualization. Integrated Cytoscape.js and related libraries. Added UI components for data upload and network settings. +69/-0    server.R Integrate network server module into main server                  R/server.R Integrated the network server module into the main server function. +2/-0      ui.R Add network interpretation tab to UI                                          R/ui.R Added a new tab for network interpretation in the UI. +1/-0

---

💡 PR-Agent usage: Comment /help "your question" on any pull request to receive relevant information

[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 Security concerns JavaScript Injection Risk: The implementation directly uses user inputs in JavaScript code generation, which could lead to JavaScript injection vulnerabilities. This is particularly concerning in lines where node and edge data are dynamically generated based on user input. Consider sanitizing these inputs or employing other methods to ensure the security of the generated code.

⚡ Recommended focus areas for review Error Handling The error handling in the CSV reading function could be improved by providing more specific error messages or handling different types of errors differently. JavaScript Injection Risk Directly using user input in JavaScript code generation could lead to JavaScript injection vulnerabilities. Consider sanitizing or validating the inputs before use. Performance Concern The loop for edge elements creation might be inefficient for large datasets. Consider optimizing this section or using vectorized operations if possible.

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Possible bug	Add error handling for data processing functions to improve robustness Consider adding error handling for the annotateProteinInfoFromIndra and getSubnetworkFromIndra functions to manage potential failures or exceptions in data processing. R/module-visualize-network-server.R [33-34] -annotated_df <- annotateProteinInfoFromIndra(df(), proteinIdType()) -subnetwork <- getSubnetworkFromIndra(annotated_df, pValue()) +annotated_df <- tryCatch({ + annotateProteinInfoFromIndra(df(), proteinIdType()) +}, error = function(e) { + showNotification(paste("Error in annotation:", e$message), type = "error") + return(NULL) +}) +subnetwork <- tryCatch({ + getSubnetworkFromIndra(annotated_df, pValue()) +}, error = function(e) { + showNotification(paste("Error in subnetwork extraction:", e$message), type = "error") + return(NULL) +}) Suggestion importance[1-10]: 8 Why: Adding error handling for the functions annotateProteinInfoFromIndra and getSubnetworkFromIndra is crucial to manage potential failures, improving the robustness and user experience of the application.	8
	Ensure numeric inputs are valid to prevent runtime errors Validate the numeric conversion of input$pValue and input$logFC to handle non-numeric inputs gracefully. R/module-visualize-network-server.R [20-28] pValue <- reactive({ req(input$pValue) - as.numeric(input$pValue) + if (!is.na(as.numeric(input$pValue))) { + as.numeric(input$pValue) + } else { + showNotification("Invalid pValue input", type = "error") + return(NULL) + } }) logFC <- reactive({ req(input$logFC) - as.numeric(input$logFC) + if (!is.na(as.numeric(input$logFC))) { + as.numeric(input$logFC) + } else { + showNotification("Invalid logFC input", type = "error") + return(NULL) + } }) Suggestion importance[1-10]: 7 Why: Validating the numeric conversion of inputs pValue and logFC is essential to prevent runtime errors from non-numeric inputs, thus enhancing the application's stability and user experience.	7
Performance	Implement caching to reduce redundant data processing and enhance responsiveness Implement caching for the df, proteinIdType, pValue, and logFC reactive expressions to improve performance by avoiding redundant computations. R/module-visualize-network-server.R [4-28] df <- reactive({ req(input$dataUpload) - tryCatch({ - read.csv(input$dataUpload$datapath) - }, error = function(e) { - ... + isolate({ + if (!exists("cached_df")) { + cached_df <<- tryCatch({ + read.csv(input$dataUpload$datapath) + }, error = function(e) { + ... + }) + } + cached_df }) }) proteinIdType <- reactive({ req(input$proteinIdType) - input$proteinIdType + isolate({ + if (!exists("cached_proteinIdType")) { + cached_proteinIdType <<- input$proteinIdType + } + cached_proteinIdType + }) }) ... Suggestion importance[1-10]: 6 Why: Implementing caching for reactive expressions can significantly enhance performance by reducing redundant computations, especially beneficial in scenarios with frequent data updates or large datasets.	6
	Use more efficient methods for generating JavaScript code to enhance performance Optimize the JavaScript code generation by using more efficient string concatenation techniques or pre-compiled templates. R/module-visualize-network-server.R [54-60] -js_code <- paste0(" - cytoscape.use(cytoscapeDagre); - var cy = cytoscape({ - container: document.getElementById('network-cy'), - elements: [", paste(elements, collapse = ", "), "], - ... - }); - ") +js_template <- "cytoscape.use(cytoscapeDagre); + var cy = cytoscape({ + container: document.getElementById('network-cy'), + elements: [%s], + ... + });" +js_code <- sprintf(js_template, paste(elements, collapse = ", ")) Suggestion importance[1-10]: 5 Why: Optimizing JavaScript code generation using templates can improve performance, although the impact might be moderate depending on the overall application size and complexity.	5

[tonywu1999]

nit: Could you rename this to visualizeNetworkServer? I think networkServer may confuse people thinking it has something to do with computer networks.

[pnavada]

done

[tonywu1999]

Code would be easier to follow if you do df$Protein <- as.character(df$Protein)

[pnavada]

done

[tonywu1999]

I think this is where we had talked about using the proteins being already filtered before coming to step 5 network viz.

I think adding a significant parameter to the returned object may not be necessary since you can use the dataComparison$ComparisonResult table as the list of proteins with their p-values

[pnavada]

done

[tonywu1999]

Any reason for removing this print statement here?

[pnavada]

added it back

[tonywu1999]

Did you add a dependency on MSstatsBioNet for this package? i.e. using the @importFrom tag and adding the dependency in the DESCRIPTION file.

[pnavada]

done

[tonywu1999]

Will this call INDRA again? If so, could we use the original edges table that we had already.

[pnavada]

No, it wont call INDRA again. It happens only when the inputs change

[tonywu1999]

rather than a string split, I believe we already have source, target, and interaction data stored in the edge as well. Would it be simpler to pass and retrieve that information instead of the ID itself?

[pnavada]

done

[tonywu1999]

Instead of passing significant, could you instead pass data_comparison$ComparisonResult instead here?

[pnavada]

done