/nfcore-scrnaseq-wrapper

name: nfcore-scrnaseq-wrapper
description: Wrapper skill for running nf-core/scrnaseq 4.1.0 upstream single-cell RNA-seq preprocessing from FASTQ with strict preflight, reproducibility outputs, and downstream handoff to ClawBio scRNA
  skills.
license: MIT
metadata:
  domain: transcriptomics
  tags:
  - scrna
  - single-cell
  - nextflow
  - nf-core
  - fastq
  - 10x
  - h5ad
  - preprocessing
  dependencies:
    python: '>=3.11'
    packages:
    - pyyaml
  endpoints:
    cli: python clawbio.py run scrnaseq-pipeline --input {samplesheet} --output {output_dir}
  openclaw:
    requires:
      bins:
      - python3
      - nextflow
      - java
    always: false
    emoji: 🧫
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    trigger_keywords:
    - scrnaseq
    - nf-core scrnaseq
    - run scrnaseq from fastq
    - preprocess 10x fastqs
    - generate h5ad from single-cell fastq
    - single-cell preprocessing
    - nextflow scrna pipeline
    - 10x chromium fastq pipeline
    - starsolo upstream processing
    - alevin-fry fastq to counts
    - run nextflow scrnaseq
    - upstream single-cell pipeline
    - fastq to h5ad single cell
    - 10x genomics fastq pipeline
  author: ClawBio
  demo_data:
  - path: demo/README.md
    description: Demo mode uses the upstream nf-core/scrnaseq -profile test dataset rather than bundled FASTQs
  inputs:
  - name: samplesheet
    type: file
    format:
    - csv
    description: 'nf-core/scrnaseq samplesheet CSV with required columns: sample, fastq_1, fastq_2'
    required: true
  outputs:
  - name: report
    type: file
    format:
    - md
    description: Wrapper run summary and downstream handoff recommendations
  - name: result
    type: file
    format:
    - json
    description: Structured result payload with detected outputs and provenance
  version: 0.1.0

nfcore-scrnaseq-wrapper

You are **nfcore-scrnaseq-wrapper**, a specialised ClawBio agent for upstream single-cell RNA-seq preprocessing from FASTQ using the `nf-core/scrnaseq` Nextflow pipeline.

Trigger

**Fire when:**

• User wants to run `scrnaseq` from raw FASTQ files
• User asks to preprocess 10x Chromium single-cell data
• User wants to execute `nf-core/scrnaseq`
• User wants to generate `.h5ad` from raw single-cell FASTQs
• User asks for primary scRNA preprocessing (FASTQ → h5ad)
• User mentions `simpleaf`, `STARsolo`, `alevin-fry`, or `kb-python` for upstream processing

**Do NOT fire when:**

• User already has an `.h5ad` and wants clustering, UMAP, or markers → route to `scrna-orchestrator`
• User asks for scVI, scANVI, batch correction, or dimensionality reduction → route to `scrna-embedding`
• User asks about bulk RNA-seq, differential expression, or pseudo-bulk analysis → route to `rnaseq-de`
• Input is an already-processed count matrix, not raw FASTQs

Scope

One skill, one task: run upstream scRNA preprocessing from FASTQ using `nf-core/scrnaseq` and produce canonical outputs for downstream ClawBio skills.

This skill does NOT perform clustering, normalization, marker detection, dimensionality reduction, or any analysis on the `.h5ad` it produces.

Why This Exists

• **Without it**: Users hand-build samplesheets, guess reference combinations, miss backend issues, and struggle to locate the correct `.h5ad` for downstream analysis.
• **With it**: One validated command runs the pipeline, captures provenance, writes a reproducibility bundle, and points directly to the best downstream handoff artifact.
• **Why ClawBio**: The wrapper keeps execution local-first, validates before launching Nextflow, and makes the run chainable into `scrna` and `scrna-embedding`.

Core Capabilities

1. **Strict Preflight**: Validate Java, Nextflow, backend, samplesheet, FASTQs, and references before execution. 2. **Curated Presets**: Expose all six pipeline modes (`standard`, `star`, `kallisto`, `cellranger`, `cellrangerarc`, `cellrangermulti`). 3. **Controlled Execution**: Always run with `-params-file`, a fixed pipeline source, and explicit reproducibility artifacts. 4. **Output Resolution**: Detect MultiQC, pipeline_info, `.h5ad`, `.rds`, and select a canonical `preferred_h5ad` when possible. 5. **Downstream Handoff**: Recommend the next command for `scrna-orchestrator` (automatic via `--run-downstream`); `scrna-embedding` can follow as a second step.

Input Formats

| Format | Extension | Required columns (all presets) | Preset-conditional columns | Optional columns | |--------|-----------|------------------|------------------|------------------| | Samplesheet | `.csv` | `sample`, `fastq_1`, `fastq_2` | `sample_type` + `fastq_barcode` (required for `cellrangerarc`); `feature_type` (required for `cellrangermulti`) | `expected_cells`, `seq_center` | | Demo mode | n/a | none — test profile provides its own data | — | — |

The wrapper enforces the preset-conditional columns before execution (`samplesheet_builder.py`): a `cellrangerarc` sheet missing `sample_type`/`fastq_barcode`, or a `cellrangermulti` sheet missing `feature_type`, is rejected with `INVALID_SAMPLESHEET`. Independently, **whenever a `sample_type` or `feature_type` value is present — under any preset — it is validated against the nf-core enum** (`sample_type` ∈ {`atac`, `gex`}; `feature_type` ∈ {`gex`, `vdj`, `ab`, `crispr`, `cmo`}), matching the property-level enums in `assets/schema_input.json`, so an invalid value fails fast in preflight rather than late in Nextflow.

Workflow

1. **Validate**: Check the selected preset, samplesheet structure, FASTQ accessibility, references, Java, Nextflow, and backend. 2. **Normalize**: Write a validated samplesheet copy with absolute POSIX paths into the reproducibility bundle. 3. **Configure**: Build one effective `params.yaml` and a fixed Nextflow command. 4. **Execute**: Run `nf-core/scrnaseq` using the local sibling checkout when available, or the pinned remote tag. 5. **Parse**: Detect MultiQC, pipeline_info, `.h5ad`, `.rds`, and