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[noamteyssier]

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[gemini-code-assist]

Summary of Changes

Hello @noamteyssier, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances the data quality and validation mechanisms within the cell-eval library. It introduces more stringent checks for log-normalized data and differential expression results, ensuring that input data conforms to expected formats and ranges. These changes aim to prevent downstream errors and provide clearer diagnostics when data inconsistencies are encountered, ultimately leading to more reliable evaluation metrics.

Highlights

• Enhanced Data Validation for Log-Normalized Data: The guess_is_lognorm utility function has been significantly improved to include more robust validation checks. It now detects negative values and values exceeding a defined maximum threshold (default 15.0) in log-normalized data, raising ValueError for invalid scales. It also adds logging to provide clearer information about the data type detected.
• Improved Differential Expression (DE) Data Integrity Checks: The DEResults dataclass now includes integrity checks during initialization. It logs warnings if null, infinite, or NaN fold change values are identified in the DE results, providing better insight into potential data quality issues.
• Conditional Log-Normalization Validation: The _convert_to_normlog function in the evaluator now conditionally skips the detailed validation of log-normalized data if discrete data is explicitly allowed, preventing unnecessary strictness in certain scenarios.
• Version Bump and IDE Ignore: The project version has been updated to 0.6.5 in pyproject.toml, and .idea/* has been added to .gitignore to prevent IDE-specific files from being committed.
• Comprehensive Unit Tests: New and expanded unit tests have been added for guess_is_lognorm and MetricsEvaluator to cover various scenarios, including valid log-normalized data, integer data, edge cases, and invalid data scales, ensuring the robustness of the new validation logic.

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[gemini-code-assist]

Code Review

This pull request introduces significant improvements to data validation and integrity checks. The guess_is_lognorm function is now more robust, validating that log-normalized data falls within an expected range, which helps prevent issues with mixed-scale data. Additionally, new checks have been added to validate the integrity of differential expression results by flagging null, infinite, or NaN values. These changes are well-supported by a comprehensive set of new unit tests. My review includes a few suggestions to improve performance and code style.