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SKILL.md
Single-Cell RNA-seq Quality Control
Automated QC workflow for single-cell RNA-seq data following scverse best practices.
When to Use This Skill
Use when users:
Request quality control or QC on single-cell RNA-seq data
Want to filter low-quality cells or assess data quality
Need QC visualizations or metrics
Ask to follow scverse/scanpy best practices
Request MAD-based filtering or outlier detection
Supported input formats:
.h5ad files (AnnData format from scanpy/Python workflows)
.h5 files (10X Genomics Cell Ranger output)
Default recommendation: Use Approach 1 (complete pipeline) unless the user has specific custom requirements or explicitly requests non-standard filtering logic.
Approach 1: Complete QC Pipeline (Recommended for Standard Workflows)
For standard QC following scverse best practices, use the convenience script scripts/qc_analysis.py:
python3 scripts/qc_analysis.py input.h5ad
# or for 10X Genomics .h5 files:
python3 scripts/qc_analysis.py raw_feature_bc_matrix.h5
The script automatically detects the file format and loads it appropriately.
When to use this approach:
Standard QC workflow with adjustable thresholds (all cells filtered the same way)
<input_basename>_filtered.h5ad - Clean, filtered dataset ready for downstream analysis
<input_basename>_with_qc.h5ad - Original data with QC annotations preserved
If copying outputs to /mnt/user-data/outputs/ for user access, copy individual files (not the entire directory) so users can preview them directly as Claude.ai artifacts.
Apply MAD-based filtering - Permissive outlier detection using MAD thresholds for counts/genes/MT%
Filter genes - Remove genes detected in few cells
Generate visualizations - Comprehensive before/after plots with threshold overlays
Approach 2: Modular Building Blocks (For Custom Workflows)
For custom analysis workflows or non-standard requirements, use the modular utility functions from scripts/qc_core.py and scripts/qc_plotting.py:
# Run from scripts/ directory, or add scripts/ to sys.path if needed
import anndata as ad
from qc_core import calculate_qc_metrics, detect_outliers_mad, filter_cells
from qc_plotting import plot_qc_distributions # Only if visualization needed
adata = ad.read_h5ad('input.h5ad')
calculate_qc_metrics(adata, inplace=True)
# ... custom analysis logic here
When to use this approach:
Different workflow needed (skip steps, change order, apply different thresholds to subsets)
Conditional logic (e.g., filter neurons differently than other cells)
Partial execution (only metrics/visualization, no filtering)
Integration with other analysis steps in a larger pipeline
Custom filtering criteria beyond what command-line params support
