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Summary of ChangesHello @julenmendieta, 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 significantly overhauls the project's technical foundation by modernizing its build pipeline and dependency management, leading to a more robust and maintainable codebase. Concurrently, it delivers substantial performance gains in data processing for dimensionality reduction tasks and introduces a key feature for biological analysis, all while enhancing the reproducibility of results. Highlights
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Code Review
This pull request introduces significant performance and memory efficiency improvements, primarily by refactoring the Python data processing scripts to use polars and a more efficient method for creating sparse matrices. A new feature for selecting Highly Variable Genes (HVG) has been added across the stack (backend, model, UI), which further enhances performance and analytical focus. The build system and dependencies have also been substantially updated for better maintainability. My review focuses on ensuring the new logic is robust and consistent. I've identified a couple of minor issues: a misleading log message and an opportunity for further memory optimization by using a consistent data type. I also noted a potential UI improvement for a more robust user experience. Overall, this is a high-quality contribution that significantly improves the block.
software/src/batch_correction.py
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This log message refers to ComBat correction, but the script has been updated to only use Harmony and no longer performs ComBat correction. This message is misleading and should be removed to avoid confusion.
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The sparse matrix is being created with dtype=np.float64. While this provides higher precision, scanpy and many single-cell analysis tools typically operate on np.float32 to conserve memory, which is particularly important for large datasets. The accompanying batch_correction.py script correctly uses np.float32. For consistency and to align with the performance goals of this PR, it would be better to use np.float32 here as well.
| dtype=np.float64 | |
| dtype=np.float32 |
| <PlNumberField | ||
| v-if="app.model.args.hvgEnabled" | ||
| v-model="app.model.args.hvgCount" | ||
| label="Number of HVG" | ||
| :min-value="app.model.args.nPCs + 1" | ||
| :step="100" | ||
| /> |
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The min-value for the "Number of HVG" is correctly bound to app.model.args.nPCs + 1. However, this only enforces the minimum at the UI level. If a user sets hvgCount and then increases nPCs to a value hvgCount - 1 or greater, the model value for hvgCount will become invalid but won't be automatically adjusted. This could lead to downstream errors.
For a more robust user experience, consider adding a watcher on app.model.args.nPCs to automatically adjust hvgCount if it becomes invalid.
import { watch } from 'vue';
watch(() => app.model.args.nPCs, (newNPCs) => {
if (app.model.args.hvgEnabled && app.model.args.hvgCount <= newNPCs) {
app.model.args.hvgCount = newNPCs + 1;
}
});2 participants
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