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name: scrna-qc 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.