scrna-seq-qc Skill
Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species. Use when Codex needs to build, adapt, or review a general scRNA-seq QC pipeline; choose dataset-appropriate cell-level filters from QC distributions; run required scDblFinder-based doublet and am Published by openai in plugins.
What is scrna-seq-qc Skill?
Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species. Use when Codex needs to build, adapt, or review a general scRNA-seq QC pipeline; choose dataset-appropriate cell-level filters from QC distributions; run required scDblFinder-based doublet and am Published by openai in plugins. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Writing
- Data analysis use cases
- Writing use cases
Technical details
- Install or run with Copy skill directory
When to use scrna-seq-qc Skill
- Use it for data analysis.
- Use it for writing.
Built with
Editorial notes
Source
- Creator: openai
- Repository: openai/plugins
- Skill file: plugins/ngs-analysis/skills/scrna-seq-qc/SKILL.md
What it does
Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species. Use when Codex needs to build, adapt, or review a general scRNA-seq QC pipeline; choose dataset-appropriate cell-level filters from QC distributions; run required scDblFinder-based doublet and am
Skill instructions
scRNA-seq QC Start Here Read references/qc-annotation-umap-heuristics.md before picking thresholds, annotation backends, or UMAP feature-selection rules. Confirm what inputs exist before writing code: - An AnnData object or equivalent with raw counts preserved. - Per-sample, per-batch, or per-channel metadata, because QC and doublet detection should respect technical partitions. - Organism, tissue, assay type, chemistry, and whether the data are whole-cell or single-nucleus. - Whether a matched cell atlas or label-transfer reference exists for the tissue and species. Preserve provenance in the output: package versions, thresholds, threshold-justification plots, counts removed or flagged at each filter, annotation backend and reference, marker-gene selection heuristic, and any manual cluster exclusions. Workflow 1. Choose QC thresholds from the data, not from a fixed template. - Plot detected genes, total UMIs, mitochondrial fraction, and any tissue-specific nuisance signals overall and
Explore related resources
Frequently asked questions
What is scrna-seq-qc?
scrna-seq-qc is a open-source AI agent skill with Copy skill directory. Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species.
Who is scrna-seq-qc best for?
scrna-seq-qc is best for reusing agent instructions, scripts, and references, data analysis workflows, writing workflows.
How do I install scrna-seq-qc?
Install or run scrna-seq-qc using Copy skill directory. Check scrna-seq-qc for the latest setup command.
Is scrna-seq-qc actively maintained?
scrna-seq-qc may need a closer maintenance check before production use.
Auto-fetched from GitHub.
