LLMs-Universal-Life-Science-and-Clinical-Skills- Single_Cell_RNA_QC

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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 reliability:
• source: https://github.com/scverse/scanpy score: 0.90 rationale: >- Scanpy repository maintained by the scverse core team with actively tested QC utilities (mito/ribo scoring, MAD filtering).
• source: https://github.com/theislab/single-cell-best-practices score: 0.88 rationale: >- Theis Lab best-practices playbook detailing MAD-based QC thresholds, mitochondrial cutoffs, and reproducibility checklists for scRNA-seq.

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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.
• GitHub provenance: Scanpy QC modules and scverse best-practices notebooks.

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