test(anomaly_scores): Add unit tests for dataProcess and groupComparison for anomaly score feature

[tonywu1999]

User description

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
• I have run the devtools::document() command after my changes and committed the added files

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PR Type

Enhancement, Tests

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Description

• Enforce linear summarization with anomaly scores

• Pass input columns into parameter checks

• Add unit tests for anomaly-weighted summarization

• Add tests for weighted groupComparison modeling

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Diagram Walkthrough

flowchart LR
  DP["dataProcess()"] --> CHK[".checkDataProcessParams()"]
  RAW["raw input colnames"] -- passed --> CHK
  CHK -- detects 'AnomalyScores' --> RULE["Require summaryMethod='linear'"]
  RULE -- error if not linear --> DP
  TEST1["test_dataProcess"] -- anomaly scenarios --> DP
  TEST2["test_groupComparison"] -- Variance as weights --> GC["groupComparison()"]
  TEST3["test_utils_groupComparison_model"] -- verify weights --> FIT[".fitModelForGroupComparison"]

Loading

File Walkthrough

	Relevant files
Enhancement	dataProcess.R Forward input columns to param validation                                R/dataProcess.R Pass colnames(raw) to parameter checker Enable anomaly-aware validation in checks +2/-1      utils_checks.R Add anomaly-aware summaryMethod validation                              R/utils_checks.R Extend .checkDataProcessParams signature with input_columns Validate summaryMethod when AnomalyScores present Emit informative error guiding to linear summarization +10/-2
Tests	test_dataProcess.R Tests for anomaly-driven linear summarization                        inst/tinytest/test_dataProcess.R Add dataset with AnomalyScores Expect error for non-linear summarization Validate variance behavior with/without anomalies Compare high vs low anomaly variance means +88/-1    test_groupComparison.R Tests for weighted groupComparison using Variance                inst/tinytest/test_groupComparison.R Add protein-level inputs with/without Variance Compare SE and p-values with anomaly-based weights Ensure unaffected proteins remain consistent +44/-1    test_utils_groupComparison_model.R Verify model weights equal inverse variance                            inst/tinytest/test_utils_groupComparison_model.R New test for .fitModelForGroupComparison Confirm weights applied as 1/Variance Ensure weights exist when Variance provided +18/-0

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✨ Finishing Touches

🧪 Generate unit tests

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• Commit unit tests in branch tony-feature-model_weights

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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 Error Message Clarity The new stop message for AnomalyScores enforces summaryMethod='linear'. Confirm wording and guidance fully align with package documentation and expected user workflow, and that it won’t trigger for columns named similarly or with different casing. if ("AnomalyScores" %in% input_columns) { if (summarization$method != "linear") { stop("AnomalyScores column detected in your input columns. ", "Please set summaryMethod to 'linear' to use anomaly scores for protein summarization, ", "or remove the AnomalyScores column from your input data.") } } Test Robustness Tests compare mean variances across synthetic datasets. Validate that randomness or environment differences can’t cause flaky behavior; consider tighter, deterministic assertions tied to specific proteins or exact expected values if stable. QuantDataLowAnomaly = dataProcess(msstats_input_low_anomaly, normalization=FALSE, MBimpute=TRUE, summaryMethod="linear") high_anomaly_variance = protein_data$Variance low_anomaly_variance = QuantDataLowAnomaly$ProteinLevelData$Variance expect_true(mean(high_anomaly_variance, na.rm = TRUE) > mean(low_anomaly_variance, na.rm = TRUE), info = "Mean variance should be higher for dataset with high anomaly scores") API Change Propagation .checkDataProcessParams gained a new parameter (input_columns). Ensure all call sites across the codebase are updated accordingly and that default behavior remains unchanged when AnomalyScores is absent. .checkDataProcessParams( logTrans, normalization, nameStandards, list(method = featureSubset, n_top = n_top_feature, remove_uninformative = remove_uninformative_feature_outlier), list(method = summaryMethod, equal_var = equalFeatureVar), list(symbol = censoredInt, MB = MBimpute), colnames(raw))

[github-actions]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix multi-part error message The error message concatenates multiple character strings with commas, which creates a vector and prints only the first element in stop(). Collapse into a single string to ensure the full message is shown. Also remove the double space after the first sentence fragment. R/utils_checks.R [99-105] if ("AnomalyScores" %in% input_columns) { if (summarization$method != "linear") { - stop("AnomalyScores column detected in your input columns. ", - "Please set summaryMethod to 'linear' to use anomaly scores for protein summarization, ", - "or remove the AnomalyScores column from your input data.") + stop(paste0( + "AnomalyScores column detected in your input columns. ", + "Please set summaryMethod to 'linear' to use anomaly scores for protein summarization, ", + "or remove the AnomalyScores column from your input data." + )) } } Suggestion importance[1-10]: 7 __ Why: In R, supplying multiple comma-separated strings to stop() creates a character vector and only the first element is used; collapsing with paste0 ensures the entire message is emitted. This is a correct, localized improvement with modest impact.	Medium
General	Sanitize input column names Passing colnames(raw) directly may include NA or duplicate names, causing false positives or errors in downstream checks. Sanitize to a unique, non-NA character vector before passing to the validator. R/dataProcess.R [139-140] .checkDataProcessParams( logTrans, normalization, nameStandards, list(method = featureSubset, n_top = n_top_feature, remove_uninformative = remove_uninformative_feature_outlier), list(method = summaryMethod, equal_var = equalFeatureVar), list(symbol = censoredInt, MB = MBimpute), - colnames(raw)) + unique(stats::na.omit(colnames(raw))) +) Suggestion importance[1-10]: 4 __ Why: Making column names unique and non-NA could prevent edge-case issues, but the PR does not indicate such problems and colnames() is typically character without NAs; the change is low-risk yet low-impact.	Low

[tonywu1999]

TODO: Add granular unit tests for data summarization function