Localized reading guide
single-cell-rna-qcPurpose, usage and source
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Source retrieved:
- Source repository
- anthropics/knowledge-work-plugins
- SKILL.md
bio-research/skills/single-cell-rna-qc/SKILL.md- Repository stars
- 34,049
- Repository updated
- 2026-10-30
- Categories
- research
- Last collected
License: License file needs review
Workflow excerpts are reading aids, not executable instructions from Codez. No third-party Skill was run to produce this page.
The purpose and workflow passages below are upstream material, organized and translated for reading. They are distinct from the separately labeled Codez editorial analysis.
What this Skill does
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
How the upstream workflow works
Approach 1: Complete QC Pipeline (Recommended for Standard Workflows)
For standard QC following scverse best practices, use the convenience script scripts/qc_analysis.py: The script automatically detects the file format and loads it appropriately. When to use this approach: Standard QC workflow with adjustable thresholds (all cells filtered the same way)
Workflow Steps
The script performs the following steps: Calculate QC metrics - Count depth, gene detection, mitochondrial/ribosomal/hemoglobin content Apply MAD-based filtering - Permissive outlier detection using MAD thresholds for counts/genes/MT% Filter genes - Remove genes detected in few cells
Upstream passage · L67 ↗Approach 2: Modular Building Blocks (For Custom Workflows)
For custom analysis workflows or non-standard requirements, use the modular utility functions from scripts/qc_core.py and scripts/qc_plotting.py: When to use this approach: Different workflow needed (skip steps, change order, apply different thresholds to subsets) Conditional logic (e.g., filter neurons differently than other cells)
python3 scripts/qc_analysis.py input.h5ad
# or for 10X Genomics .h5 files:
python3 scripts/qc_analysis.py raw_feature_bc_matrix.h5Requirements and setup
No short requirements passage was selected from this file. This does not mean the Skill has no dependencies. Check the repository setup instructions and referenced files.
Limits and quality checks
Single-Cell RNA-seq Quality Control
Automated QC workflow for single-cell RNA-seq data following scverse best practices.
Upstream passage · L6 ↗Start with a safe trial
- Check the supported host and the repository installation instructions. Keep the Skill directory and its referenced scripts, assets and documentation together; SKILL.md alone may omit dependencies.
- Give the agent your real input, desired output and acceptance criteria. Invocation syntax and available tools depend on the host; this site does not assume a universal install command.
- Try the source workflow in a disposable project or on a copy. Review operations that change files, remote services or billable resources before allowing them.
- Validate the output against the source quality checks and your own requirements. A successful trigger or a high repository Star count does not establish a correct result.
Read further in the source
Related Skills
Translation and scope
This reading guide selects passages from the pinned upstream SKILL.md. Translation is assisted. It is not a complete manual or a claim of tested compatibility. Read the original and its referenced files before use.
This introduction refers to the pinned version below; current repository activity and collection time are shown separately.
- Pinned source
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- Source retrieved
- Source commit
2574e29377c7173d0cecffef29a6ecbd7f8ccb5d- Source SHA-256
49cb573916deec6fcf6cc2cb1b573469cbc8c34b84326597ce2db22d0202393f