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sc-count

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Load when turning scRNA FASTQ (or existing CellRanger/STARsolo/SimpleAF/kb-python output) into a downstream-ready AnnData. Skip when reads are already counted into AnnData (use sc-standardize-input) or for raw quality assessment only (use sc-fastq-qc).

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sc-count

When to use

The user has FASTQ files (or pre-existing tool output directories) and wants per-cell counts in OmicsClaw's canonical AnnData contract. Four backends share one CLI: cellranger, starsolo, simpleaf, kb-python. When passed an already-counted directory the skill re-canonicalises rather than re-counts. Pairs with sc-fastq-qc upstream (read QC) and sc-multi-count downstream (merging multiple samples).

Inputs & Outputs

InputFormatRequired
Reads or countsFASTQ path / dir, or existing CellRanger / STARsolo / SimpleAF / kb-python output diryes (unless --demo)
Referencetranscriptome / genome dir / index, depending on backendonly when running the backend (not for re-canonicalising existing output)
OutputPathNotes
AnnDataprocessed.h5adcanonical contract; sample-label populated when --sample is used
Run summarytables/count_summary.csvcompact run-level summary
Backend summarytables/backend_summary.csvbackend-emitted metric/value pairs (always written)
Per-barcode metricstables/barcode_metrics.csvper-barcode count summary
Diagnostic figuresfigures/barcode_rank.png, figures/count_distributions.png, figures/count_complexity_scatter.pngalways rendered
Reportreport.md + result.jsonalways written

Flow

  1. Resolve --input; if it's an existing CellRanger / STARsolo / SimpleAF / kb-python output dir, re-canonicalise instead of running the backend.
  2. Otherwise validate backend prerequisites (chemistry, reference, t2g for kb-python, whitelist for STARsolo).
  3. Run the chosen backend against the FASTQ (and --read2 if explicit).
  4. Load the resulting matrix into AnnData; canonicalise (layers["counts"], adata.raw, gene-name harmonisation).
  5. Render barcode-rank + count-distribution figures.
  6. Emit processed.h5ad + report.md + result.json.

Gotchas

  • Missing input path → hard fail. sc_count.py:356 raises FileNotFoundError(f"Input path not found: {input_path}"). Common when the FASTQ dir is on a network mount that has not been resolved at run time.
  • STARsolo requires explicit chemistry. sc_count.py:420 raises ValueError("STARsolo runs require an explicit --chemistryvalue such as10xv3.") when chemistry is left at the auto default. STARsolo currently supports 10xv2, 10xv3, and 10xv4; pass one of those.
  • Backend prerequisites are validated up front. sc_count.py:401, :423, :451 raise ValueError for missing --reference (CellRanger/STARsolo/simpleaf), missing --t2g (kb-python), or unsupported --chemistry for STARsolo. No silent fallback to a different backend — pick a feasible one before invoking.
  • Re-canonicalising-existing-output is detected by directory shape, not a flag. If --input points at a CellRanger output dir (e.g. one with outs/raw_feature_bc_matrix/), the skill skips counting and just imports the matrix. No flag separates the two paths; verify by inspecting result.json["data"]["execution"] (empty list = re-canonicalise; populated = backend invoked) or by reading tables/backend_summary.csv (lists the backend metrics only when the backend ran).

Key CLI

# Demo (synthetic FASTQ + CellRanger-shaped output)
python omicsclaw.py run sc-count --demo --output /tmp/sc_count_demo

# CellRanger over FASTQ
python omicsclaw.py run sc-count \
  --input fastq_dir/ --output results/ \
  --reference cellranger_transcriptome --threads 16

# STARsolo (requires explicit chemistry)
python omicsclaw.py run sc-count \
  --input fastq_dir/ --output results/ \
  --reference star_genome_dir --chemistry 10xv3 --whitelist barcodes.tsv

# Re-canonicalise an existing CellRanger output directory
python omicsclaw.py run sc-count \
  --input cellranger_output_dir/ --output results/

See also

  • references/parameters.md — every CLI flag and per-backend prerequisite
  • references/methodology.md — backend selection guide, re-canonicalise vs re-run logic
  • references/output_contract.md — processed.h5ad schema + table layouts
  • Adjacent skills: sc-fastq-qc (upstream — read-quality check before counting), sc-multi-count (downstream — merge multiple sample outputs), sc-standardize-input (parallel — for AnnData from outside OmicsClaw)