Sc Filter

Scope Boundary

This skill currently exposes one public workflow: threshold_filtering.

This skill does:

1. use existing QC metrics or compute the ones needed for thresholding
2. filter cells by genes, counts, and mitochondrial percentage
3. filter genes by minimum detected-cell count
4. export filtered AnnData, figures, tables, and a report

This skill does not:

1. normalize counts
2. run HVG, PCA, UMAP, or clustering
3. remove doublets or ambient RNA

Input Formats

Input Expectations

• Accepted input: .h5ad
• Expected data state: either count-oriented AnnData or QC-annotated AnnData
• Important columns when present: n_genes_by_counts, total_counts, pct_counts_mt
• If QC metrics are missing but raw counts are still recoverable, the wrapper canonicalizes the object and computes the minimum needed QC metrics automatically
• If the object already has QC metrics, the wrapper reuses them instead of forcing the user back through sc-qc

Workflow

1. Load AnnData and inspect available QC columns plus matrix state.
2. Reuse existing QC metrics when present; otherwise canonicalize count-like input and compute the minimum needed QC metrics.
3. Apply optional tissue preset and resolve effective thresholds.
4. Filter cells by configured thresholds.
5. Filter genes by min_cells.
6. Write processed.h5ad, figures, figure-data CSVs, summary tables, report.md, and result.json.

CLI Reference

python skills/singlecell/scrna/sc-filter/sc_filter.py \
  --input <data.h5ad> --output <dir>

python skills/singlecell/scrna/sc-filter/sc_filter.py \
  --input <data.h5ad> --tissue pbmc --output <dir>

python skills/singlecell/scrna/sc-filter/sc_filter.py \
  --input <data.h5ad> --min-genes 200 --max-genes 6000 \
  --max-mt-percent 15 --min-cells 3 --output <dir>

Public Parameters

Algorithm / Methodology

Current OmicsClaw threshold_filtering always:

1. loads the AnnData object
2. checks whether QC metrics already exist
3. resolves optional tissue presets into effective thresholds
4. filters cells by genes, counts, and mitochondrial percentage
5. filters genes by min_cells
6. exports filtered data plus summary artifacts

Important implementation note:

• tissue is an OmicsClaw wrapper preset, not an upstream Scanpy parameter.

Output Contract

Successful runs write:

• processed.h5ad
• report.md
• result.json
• figures/
• tables/filter_stats.csv
• tables/filter_summary.csv
• tables/retention_summary.csv
• figure_data/
• reproducibility/commands.sh

Visualization Contract

The current wrapper writes a standard recipe-driven gallery:

• figures/filter_comparison.png
• figures/filter_summary.png

What Users Should Inspect First

1. report.md
2. figures/filter_comparison.png
3. tables/filter_stats.csv
4. figures/filter_summary.png
5. processed.h5ad

Current Limitations

• This wrapper now writes README.md and notebook-style reproducibility artifacts when notebook export dependencies are available.
• Threshold presets are OmicsClaw wrapper defaults, not upstream standard recommendations for every tissue.

Safety And Guardrails

• Explain the effective thresholds before running, especially when --tissue presets are used.
• Treat tissue presets as wrapper heuristics rather than universal biology rules.
• Doublet removal is automatic when predicted_doublet or doublet_score columns (written by sc-doublet-detection) are present in adata.obs. Pass --no-remove-doublets to disable. Use --doublet-score-threshold (default 0.25) to tune the score cutoff when only doublet_score is available.
• For short execution guardrails, see knowledge_base/knowhows/KH-sc-filter-guardrails.md.
• For longer method and interpretation guidance, see knowledge_base/skill-guides/singlecell/sc-filter.md.

Workflow Position

• Upstream step: sc-qc for QC visualization (recommended), or directly from sc-count / sc-multi-count
• Usual next step: sc-preprocessing for normalization, HVG selection, and PCA
• Optional before preprocessing: sc-doublet-detection or sc-ambient-removal

CLI Parameters

R Enhanced Plots