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sc-markers

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Load when ranking cluster-level marker genes from a clustered single-cell AnnData via Scanpy Wilcoxon / t-test / logreg or COSG specificity. Skip when comparing condition-vs-control with replicates (use sc-de) or for assigning cell-type labels (use sc-cell-annotation).

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sc-markers

When to use

The user already has clustering / cell-type labels in obs (typically leiden, louvain, or cell_type) and wants ranked marker genes per group as evidence for downstream annotation or interpretation. Four methods: wilcoxon (default rank-sum), t-test (Welch), logreg (multinomial logistic regression — discriminative ranking), cosg (fast cosine-specificity scoring without p-values). This is for cluster markers, not condition contrasts — for treatment-vs-control DE with replicates use sc-de.

Inputs & Outputs

InputFormatRequired
Clustered AnnData.h5ad with normalised expression in .X and a grouping column in obsyes (unless --demo)
OutputPathNotes
AnnData (cleaned)processed.h5addrops uns["rank_genes_groups"] / uns["rank_genes_groups_filtered"] to keep file small; preserves obs/obsm
All markerstables/markers_all.csvone row per (group, gene) — fold-change, scores, fractions, optional p-values
Top per grouptables/markers_top.csvtop---n-top per group used by figures
Per-cluster summarytables/cluster_summary.csvmarker counts + top genes per group
Marker figuresfigures/markers_heatmap.png, figures/markers_dotplot.png, figures/marker_effect_summary.png, figures/marker_cluster_summary.png, figures/marker_fraction_scatter.pngalways (last is fraction-only)
Reportreport.md + result.jsonalways

Flow

  1. Load AnnData; resolve --groupby (auto-detect from leiden / louvain / cell_type if unset).
  2. Validate parameters (n_top ≥ 1, fractions in [0, 1], mu in [0, 1]).
  3. Run the selected ranker against adata.X (treated as normalised expression).
  4. Apply post-filters (--min-in-group-fraction, --min-fold-change, --max-out-group-fraction).
  5. Build top-N table, per-cluster summary, and figure-data CSVs.
  6. Save processed.h5ad (with rank_genes_groups* purged from uns), tables/, figures/, report.md, result.json.

Gotchas

  • --groupby auto-detection requires a recognised column. sc_markers.py:135 raises ValueError("Grouping column '...' not found in adata.obs") for an explicit-but-missing key; :137 raises ValueError('No cluster/cell-type grouping column available for marker discovery.') when nothing among leiden / louvain / cell_type exists. Run sc-clustering first or pass --groupby <real-obs-column>.
  • cosg returns no p-values. sc_markers.py:91-94 registers cosg as a cosine-similarity specificity scorer — tables/markers_all.csv will lack pvals / pvals_adj columns. Downstream filters that branch on adjusted p-value must handle the method == cosg case.
  • --mu is cosg-only. sc_markers.py:373 sets result.json["mu"] = args.mu if method == 'cosg' else None. Passing --mu with another method silently records None.
  • adata.X is treated as normalised expression with no guard. sc_markers.py sets expression_source = 'adata.X' without verifying .X is log-normalised. If .X still holds raw counts (e.g., the user skipped sc-preprocessing), the Wilcoxon / t-test runs on counts and the rankings are unreliable.
  • --input is mandatory unless --demo. sc_markers.py:316 raises ValueError('--input required when not using --demo').

Key CLI

# Demo (built-in PBMC3K with leiden labels)
python omicsclaw.py run sc-markers --demo --output /tmp/sc_markers_demo

# Default Wilcoxon on leiden clusters
python omicsclaw.py run sc-markers \
  --input clustered.h5ad --output results/ --groupby leiden

# COSG fast specificity ranking on a labelled AnnData
python omicsclaw.py run sc-markers \
  --input annotated.h5ad --output results/ \
  --groupby cell_type --method cosg --mu 1.0

# Strict marker filtering (high fold-change, low out-group fraction)
python omicsclaw.py run sc-markers \
  --input clustered.h5ad --output results/ \
  --min-fold-change 1.0 --max-out-group-fraction 0.2

See also

  • references/parameters.md — every CLI flag and per-method tuning hint
  • references/methodology.md — Wilcoxon vs t-test vs logreg vs COSG; when each wins
  • references/output_contract.md — markers_all.csv column schema; figures' figure_data CSVs
  • Adjacent skills: sc-clustering (upstream — produces the leiden / louvain column), sc-cell-annotation (downstream — uses these markers as evidence for label assignment), sc-de (parallel — replicate-aware condition contrasts, NOT cluster markers)