refactor: Make changes from feedback from HUPO

[tonywu1999]

User description

Motivation and Context

Please include relevant motivation and context of the problem along with a short summary of the solution.

Changes

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Testing

Please describe any unit tests you added or modified to verify your changes.

Checklist Before Requesting a Review

• I have read the MSstats contributing guidelines
• My changes generate no new warnings
• Any dependent changes have been merged and published in downstream modules
• Ran styler::style_pkg(transformers = styler::tidyverse_style(indent_by = 4))
• Ran devtools::document()

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PR Type

Enhancement, Tests, Documentation

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Description

• Added input validation for getSubnetworkFromIndra to handle large datasets.

• Enhanced visualizeNetworks with new layout and updated color scheme.

• Introduced new utility functions for populating protein-related information.

• Expanded test coverage for input validation in getSubnetworkFromIndra.

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Changes walkthrough 📝

	Relevant files
Enhancement	4 files NAMESPACE Added new imports for layout and validation functions        +1/-0      getSubnetworkFromIndra.R Added input validation for large datasets                                +1/-0      utils_getSubnetworkFromIndra.R Introduced input validation utility for subnetwork function +10/-0    visualizeNetworks.R Enhanced network visualization with new layout and colors +5/-3
Documentation	10 files annotateProteinInfoFromIndra.R Updated parameter name in function documentation                  +1/-1      annotateProteinInfoFromIndra.Rd Updated documentation for `annotateProteinInfoFromIndra`  +4/-1      dot-populateHgncIdsInDataFrame.Rd Added documentation for HGNC ID population utility              +17/-0    dot-populateHgncNamesInDataFrame.Rd Added documentation for HGNC name population utility          +17/-0    dot-populateKinaseInfoInDataFrame.Rd Added documentation for kinase information population utility +17/-0    dot-populatePhophataseInfoInDataFrame.Rd Added documentation for phosphatase information population utility +17/-0    dot-populateTranscriptionFactorInfoInDataFrame.Rd Added documentation for transcription factor information utility +17/-0    dot-populateUniprotIdsInDataFrame.Rd Added documentation for Uniprot ID population utility        +20/-0    dot-validateAnnotateProteinInfoFromIndraInput.Rd Added documentation for input validation utility                  +17/-0    getSubnetworkFromIndra.Rd Updated documentation for `getSubnetworkFromIndra`              +4/-3
Tests	1 files test-getSubnetworkFromIndra.R Added test for input validation in `getSubnetworkFromIndra` +12/-0

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[github-actions]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

🧪 PR contains tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Input Validation The new .validateGetSubnetworkFromIndraInput function introduces a hard limit of 400 proteins. Ensure this limit is appropriate and aligns with the intended use cases, as it may restrict larger datasets. #' Validate input for MSstatsBioNet getSubnetworkFromIndra #' @param input dataframe from MSstats groupComparison output #' @keywords internal #' @noRd .validateGetSubnetworkFromIndraInput <- function(input) { if (nrow(input) >= 400) { stop("Invalid Input Error: INDRA query must contain less than 400 proteins. Consider adding a p-value cutoff") } } Visualization Color Scheme The updated color scheme in visualizeNetworks changes the visual representation of nodes. Confirm that the new colors (#ADD8E6, #D3D3D3, #FFA590) are suitable and improve clarity for users. VISUAL_STYLE_MAPPINGS <- list( mapVisualProperty("Node Label", "id", "p"), mapVisualProperty( "Node Fill Color", "logFC_color", "c", c(-logfcCutoff, 0.0, logfcCutoff), c("#ADD8E6", "#ADD8E6", "#D3D3D3", "#FFA590", "#FFA590") ) ) createVisualStyle( VISUAL_STYLE_NAME, DEFAULT_VISUAL_STYLE, VISUAL_STYLE_MAPPINGS ) setVisualStyle(VISUAL_STYLE_NAME) layoutNetwork("cose") } else {

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Score
Possible issue	Add validation to ensure required columns exist in the input before processing Ensure that the input parameter in .validateGetSubnetworkFromIndraInput is validated for required columns like HgncId before checking the row count, as missing columns could lead to runtime errors. R/utils_getSubnetworkFromIndra.R [6-9] +if (!"HgncId" %in% colnames(input)) { + stop("Invalid Input Error: Input must contain a column named 'HgncId'.") +} if (nrow(input) >= 400) { stop("Invalid Input Error: INDRA query must contain less than 400 proteins. Consider adding a p-value cutoff") } Suggestion importance[1-10]: 9 Why: The suggestion addresses a critical issue by ensuring that the required column 'HgncId' exists in the input data frame before further processing. This prevents potential runtime errors and improves the robustness of the function.	9
General	Add a test case to ensure the function validates the presence of required columns in the input data frame Include a test case to validate that the getSubnetworkFromIndra function throws an error when the input data frame is missing required columns like HgncId. tests/testthat/test-getSubnetworkFromIndra.R [27-37] -test_that("Exception is thrown for 400+ proteins in dataframe", { - input_400 <- data.frame( - Protein = paste0("Protein", 1:400), - issue = NA, +test_that("Exception is thrown for missing required columns in dataframe", { + input_missing_col <- data.frame( + Protein = paste0("Protein", 1:10), adj.pvalue = 0.05 ) expect_error( - getSubnetworkFromIndra(input_400), - "Invalid Input Error: INDRA query must contain less than 400 proteins. Consider adding a p-value cutoff" + getSubnetworkFromIndra(input_missing_col), + "Invalid Input Error: Input must contain a column named 'HgncId'." ) }) Suggestion importance[1-10]: 8 Why: Adding this test case enhances the test coverage and ensures that the function correctly handles cases where required columns are missing, thereby improving reliability and robustness.	8
	Adjust the color mapping to ensure it aligns with the expected range of values for visualization Verify that the color mapping in mapVisualProperty for "Node Fill Color" is consistent with the expected range of logFC_color, as the added color values may not align with the intended visualization logic. R/visualizeNetworks.R [59] -c("#ADD8E6", "#ADD8E6", "#D3D3D3", "#FFA590", "#FFA590") +c("#ADD8E6", "#D3D3D3", "#FFA590") Suggestion importance[1-10]: 7 Why: The suggestion improves the visualization logic by aligning the color mapping with the expected range of values, enhancing the clarity and accuracy of the visual output. However, it is less critical than addressing functional errors.	7

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 85.93%. Comparing base (90fc510) to head (6d04c6a).

Additional details and impacted files

@@            Coverage Diff             @@
##            devel      #26      +/-   ##
==========================================
+ Coverage   85.66%   85.93%   +0.26%     
==========================================
  Files           6        6              
  Lines         314      320       +6     
==========================================
+ Hits          269      275       +6     
  Misses         45       45              

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