patient-data-ops

name	patient-data-ops
description	Guide for managing patient data and bioinformatics datasets (h5ad, CSV). Standardizes data loading, preprocessing, and path management.

Patient Data Operations

This skill ensures consistent handling of synthetic patient data and bioinformatics datasets.

Data Hierarchy

Always use these standard paths:

• data/patient-data/: Individual patient .h5ad or .csv files (e.g., PAT001-OVC-2025/)
• data/reports/: Generated analysis reports (PDF/Markdown)
• data/images/: Visualizations (UMAP, Heatmaps, PCA)

Handling scRNA-seq Data (.h5ad)

The perturbation server (mcp-perturbation) works with AnnData objects for GEARS predictions. When working in that context:

1. Normalization: Ensure scanpy.pp.normalize_total and scanpy.pp.log1p are applied
2. HVG Selection: Standardize on 7,000 Highly Variable Genes (HVG) for GEARS compatibility
3. Metadata: Preserve cell_type and condition keys in .obs

Data Preprocessing Tools

Use these standard scripts where possible:

• generate_synthetic_data.py: For creating test-safe patient mocks
• data_loader.py: Use the loaders in src/mcp_[name] to ensure consistent I/O

Data Privacy & Security

• Anonymization: Never store PII (Patient Identifiable Information) in the repo
• Mocking: Always use synthetic data for unit tests
• Synthetic only: This repo contains only synthetic patient data (HIPAA safe)

Data Quality Checklist

Before running simulations:

• Check for NaN values in expression matrices
• Verify cell-type labels match the project ontology
• Ensure the file size fits within the 4Gi Cloud Run limit

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Use this skill when:

• Loading new patient datasets
• Preprocessing scRNA-seq files for GEARS (perturbation server context)
• Creating test data for bioinformatics tools