scrna-qc

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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.

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