ngs-scrna-seq

# 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 ```bash python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline scrnaseq --emit-install-plan ``` ## Kickoff Pattern nf-core preflight run: ```bash 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: ```bash 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.