single-cell-rna-qc

name: single-cell-rna-qc description: Perform quality control on single-cell RNA-seq data (.h5ad, 10x .h5, or 10x directories) using scverse best practices, MAD-based filtering (log1p counts/genes, high-tail MT%), and generate filtered AnnData plus QC plots and summary JSON. Use when users request scRNA-seq QC, filtering low-quality cells, data quality assessment, or QC visualizations. measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

Single-Cell RNA-seq Quality Control

Automated QC workflow for single-cell RNA-seq data following scverse best practices.

Approach 1: Complete QC Pipeline (Recommended)

Use the convenience script scripts/qc_analysis.py for end-to-end QC:

python3 scripts/qc_analysis.py input.h5ad
python3 scripts/qc_analysis.py raw_feature_bc_matrix.h5
python3 scripts/qc_analysis.py /path/to/10x_directory/

When to use this approach:

• Standard QC workflow with dataset-adaptive thresholds
• Batch processing or quick exploratory analysis
• Users want a reproducible, one-command pipeline

Key parameters:

• --mad-counts, --mad-genes, --mad-mt - MAD thresholds
• --mt-threshold - Hard mitochondrial % cutoff
• --min-cells - Gene filtering threshold
• --mt-pattern, --ribo-pattern, --hb-pattern - Gene patterns
• --no-log1p - Disable log1p transform for MAD on counts/genes

Outputs (in <input_basename>_qc_results/ by default):

• qc_metrics_before_filtering.png
• qc_filtering_thresholds.png
• qc_metrics_after_filtering.png
• <input_basename>_filtered.h5ad
• <input_basename>_with_qc.h5ad
• qc_summary.json

Approach 2: Modular Building Blocks (Custom Workflows)

For custom analysis workflows, use functions from scripts/qc_core.py and scripts/qc_plotting.py:

import anndata as ad
from qc_core import calculate_qc_metrics, build_qc_masks, filter_cells

adata = ad.read_h5ad('input.h5ad')
calculate_qc_metrics(adata, inplace=True)

masks = build_qc_masks(
    adata,
    mad_counts=5,
    mad_genes=5,
    mad_mt=3,
    mt_threshold=8,
    counts_transform='log1p',
    genes_transform='log1p'
)

adata_filtered = filter_cells(adata, masks['pass_qc'])

When to use this approach:

• Non-standard filtering logic (subset-specific thresholds)
• Partial execution (metrics only, plots only)
• Integration with larger pipelines

Best Practices

1. Use log1p for counts/genes - Stabilizes MAD thresholds for heavy-tailed distributions.
2. Filter high MT% only - Low MT% is usually not problematic.
3. Inspect plots - Validate that filtering aligns with biology and tissue context.
4. Be permissive by default - Preserve rare cell populations; filter further later if needed.

Reference Materials

For deeper rationale, parameter guidance, and troubleshooting, see:

• references/scverse_qc_guidelines.md

Next Steps After QC

• Ambient RNA correction (SoupX, CellBender)
• Doublet detection (scDblFinder, scrublet)
• Normalization and downstream analysis