Imported from openai/plugins (plugins/ngs-analysis/skills/ngs-scrna-seq/SKILL.md). Install upstream with npx skills add openai/plugins --skill ngs-scrna-seq. Copyright stays with the author.

Single-cell RNA-seq

Use this skill for scRNA-seq or snRNA-seq kickoff from FASTQs, Cell Ranger-style outputs, matrices, .h5, .h5ad, or .rds. This skill owns upstream intake and FASTQ-to-count routing; post-count QC, annotation, clustering, and UMAPs must route to the embedded scrna-seq-qc skill.

Essential Inputs

Confirm:

• input type: FASTQ, count matrix, .h5, .h5ad, or .rds
• assay: single-cell or single-nucleus
• chemistry or barcode/UMI layout
• organism and reference
• expected cells per sample when available
• sample, donor, batch, and channel metadata
• desired endpoint: count matrix only, QC, clustering, annotation, UMAP, or differential abundance/expression

Public Default

For FASTQs, prefer public alternatives:

• nf-core/scrnaseq
• STARsolo
• kallisto-bustools via kb-python
• alevin-fry

Use 10x Cell Ranger only when the user explicitly wants vendor-standard output and has accepted the 10x EULA.

Implementation Sequence

Treat scRNA as three ordered rows in the plugin state and execute them sequentially:

1. FASTQ-to-count: count matrix generation, barcode and feature tables, chemistry or whitelist choice, and a backend summary.
2. Post-count QC and annotation: raw-count-preserving objects, QC metrics, threshold plots, doublet and ambient-RNA outputs, clustering, UMAPs, and annotation confidence.
3. Downstream stats: pseudobulk matrices, differential expression or abundance tables, and per-condition plots.

Cell Ranger is an optional backend when vendor-standard output is explicitly required. It is not a standalone roadmap row and it is not the default execution target.

For post-count QC/annotation, use the embedded skills/scrna-seq-qc guidance. Route to that skill whenever the requested endpoint starts from a matrix, .h5, .h5ad, .rds, Cell Ranger output, or asks for QC, doublets, ambient RNA, annotation, clustering, UMAPs, or post-count differential summaries.

Preflight

python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline scrnaseq --emit-install-plan

Kickoff Pattern

nf-core preflight run:

python plugins/ngs-analysis/scripts/run_nfcore_pipeline.py \
  --pipeline scrnaseq \
  --sample-sheet samplesheet.csv \
  --profile docker \
  --genome GRCh38 \
  --bundle-root grch38_core=/refs/GRCh38

This adapter captures the generated params, pinned Nextflow command, resource gate, trace/report paths, run manifest, and visualization index in the standard plugin envelope. Add --revision <tag> for pinned nf-core execution and --execute only when Nextflow plus a container/HPC profile are ready.

Plugin-owned local execution:

python plugins/ngs-analysis/scripts/run_scrnaseq_fastq_to_count.py \
  --sample-sheet samplesheet.csv \
  --genome-fasta reference/genome.fa \
  --annotation-gtf reference/genes.gtf \
  --cb-whitelist reference/whitelist.txt \
  --execute

The FASTQ-to-count runner emits advisory resources/resource_plan.json, resource_manifest.tsv, resource_env.sh, and resource_readiness.md outputs by default. Add --genome-build, --bundle-root <bundle>=<path>, and --require-resource-plan when STARsolo reference bundle completeness should block readiness.

Matrix-level QC should be handled by scrna-seq-qc and must preserve raw counts, per-sample metadata, filter decisions, doublet calls, ambient-RNA handling, and plot outputs.

Guardrails

• Do not assume 10x chemistry from filenames alone.
• Do not silently skip doublet or ambient-RNA assessment when doing QC.
• Do not over-annotate clusters without matched references or clear markers.