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sc-filter

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Load when removing low-quality cells and lowly-detected genes from a single-cell AnnData using QC-derived thresholds or tissue presets. Skip for the full normalize→HVG→PCA→cluster pipeline (use sc-preprocessing) or when reads are still raw FASTQ (use sc-fastq-qc → sc-count first).

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sc-filter

When to use

The user has reviewed sc-qc output and now wants to actually drop low-quality cells and lowly-detected genes — by per-cell thresholds (--min-genes, --max-genes, --max-mt-percent, --min-counts, --max-counts, --min-cells) or tissue-specific presets (--tissue brain / pbmc / etc.). This skill removes cells; it does not normalise, cluster, or annotate.

Inputs & Outputs

InputFormatRequired
Single-cell AnnData.h5adyes (unless --demo)
OutputPathNotes
Filtered AnnDataprocessed.h5adpost-filter, contract preserved
Filter statstables/filter_stats.csvper-rule keep/drop counts
Retention summarytables/filter_summary.csvworkflow + threshold metadata + cells/genes-retained pct
Diagnostic figuresfigures/filter_comparison.png, figures/filter_summary.pngbefore/after panels
Provenanceresult.jsonsummary includes expression_source, warnings
Reportreport.mdalways written

Flow

  1. Load AnnData via shared loader; persist expression_source in result.json.
  2. If --tissue is set, apply preset thresholds (overrides any matching CLI flag silently).
  3. Compute per-cell metrics; mark cells / genes failing each rule.
  4. Drop cells failing any active rule; drop genes detected in fewer than --min-cells cells.
  5. Emit before/after retention tables and figures.
  6. Save processed.h5ad + report.md + result.json.

Gotchas

  • --tissue presets silently override matching CLI flags. Passing --tissue pbmc plus --max-mt-percent 30 resolves to whatever the PBMC preset declares for max_mt_percent, not 30. When mixing, omit the explicit flag or override the preset by editing it in references/methodology.md. Result tables record the effective thresholds, not the user-passed ones.
  • QC metrics are computed on demand if missing. sc_filter.py:617-622 calls ensure_qc_metrics(...) when the AnnData lacks n_genes_by_counts / pct_counts_mt, so this skill works without a prior sc-qc run. Running sc-qc first is still recommended for diagnostic figures, but it's not a hard prerequisite — the routing description used to overstate this.
  • Input file missing → hard fail. sc_filter.py:573 raises FileNotFoundError on a non-existent --input. Common in batch pipelines when an upstream output dir was renamed.
  • expression_source is recorded but does not gate the filter. result.json["summary"]["expression_source"] carries which matrix the metrics came from (layers.counts / adata.raw / adata.X). Filtering still runs even if the source is log-normalised — but total_counts / mt% interpretations become meaningless. Check the source before relying on the thresholds.
  • processed.h5ad is contract-preserving, not contract-canonical. The skill keeps whatever layers / raw / uns the input had; if upstream skipped sc-standardize-input, downstream skills may still mis-classify the count source. Run sc-standardize-input before sc-filter when input came from outside OmicsClaw.

Key CLI

# Demo
python omicsclaw.py run sc-filter --demo --output /tmp/sc_filter_demo

# Threshold-based (typical PBMC defaults)
python omicsclaw.py run sc-filter \
  --input qc_output.h5ad --output results/ \
  --min-genes 200 --max-mt-percent 20 --min-cells 3

# Tissue preset (overrides matching CLI flags)
python omicsclaw.py run sc-filter \
  --input qc_output.h5ad --output results/ --tissue pbmc

See also

  • references/parameters.md — every CLI flag and tuning hint
  • references/methodology.md — tissue preset definitions, threshold semantics
  • references/output_contract.md — processed.h5ad + table schemas
  • Adjacent skills: sc-qc (upstream — produces metrics; recommended before this), sc-doublet-detection (parallel — drops doublets), sc-preprocessing (downstream — normalise/HVG/PCA on the filtered AnnData)