scrna-qc

$npx mdskill add swaruplab/operon/scrna-qc

Run MAD-based QC on scRNA-seq data to filter low-quality cells and generate reports.

  • Accepts .h5ad, 10x .h5, or 10x directory inputs for QC processing.
  • Uses qc_analysis.py CLI or qc_core helpers to compute metrics and apply MAD thresholds.
  • Adjusts mitochondrial thresholds based on tissue context to avoid over-filtering rare populations.
  • Produces filtered .h5ad files, before/after plots, and a qc_summary.json with parameters.

SKILL.md

.github/skills/scrna-qcView on GitHub ↗
---
name: scrna-qc
display_name: "scRNA-seq QC: Fast MAD Autopilot"
description: Execute the MAD-based single-cell RNA-seq QC workflow (scripts + Python API) to filter low-quality cells and emit reports plus filtered AnnData files.
measurable_outcome: Produce filtered .h5ad files, before/after plots, and qc_summary.json within 20 minutes per dataset.
allowed-tools:
  - read_file
  - run_shell_command
---

## At-a-Glance
- **description (10-20 chars):** QC autopilot
- **keywords:** scRNAseq, MAD, h5ad, QC, plots

## Workflow
1. Accept `.h5ad`, 10x `.h5`, or 10x directory inputs; set mitochondrial/ribosomal patterns as needed.
2. Run `qc_analysis.py` (CLI) or call `qc_core` helpers to compute metrics, apply MAD thresholds, and filter cells/genes.
3. Generate standard plots (metrics before/after, threshold overlays) plus filtered data artifacts.
4. Document parameters (mad_counts/genes/mt, mt_threshold, min_cells, log1p flag) inside the summary JSON.
5. Provide guidance on next steps (doublet detection, downstream analysis).

## Guardrails
- Adjust MT% expectations for tissue context; avoid over-filtering rare populations.
- This workflow is QC only—doublet handling and batch correction stay separate.
- Keep reproducibility by storing command invocations and environment info.

## References
- See `README.md`, `qc_core.py`, `qc_analysis.py`, and `qc_plotting.py` for API usage and schema details.

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