sc-consensus-clustering

Research

Multi-resolution typed consensus over sc-clustering. Fans out leiden / louvain at several resolutions in parallel, scores members by silhouette + cross-method NMI, runs kmode / weighted / LCA consensus on the surviving base clusterings, and emits a verified report carrying the mandatory A-path banner per ADR 0010.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/TianGzlab/OmicsClaw/blob/HEAD/skills/singlecell/scrna/sc-consensus-clustering/SKILL.md

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sc-consensus-clustering

When to use

The user has a preprocessed scRNA AnnData (PCA + neighborhood graph already computed via sc-preprocessing) and wants robust cell-cluster assignments insensitive to the chosen resolution. Single-resolution Leiden/Louvain results are notoriously resolution-sensitive — at r=0.4 you get 6 broad types, at r=1.5 you get 22 sub-states. This skill runs a SACCELERATOR-style consensus across a resolution sweep (and optionally across leiden vs louvain) and reports the stable core of the labels.

It does NOT replace sc-clustering; it wraps it.

Inputs & Outputs

Input	Format	Required
Preprocessed AnnData	`--input <preprocessed.h5ad>` (with `obsm["X_pca"]`)	yes
Output directory	`--output <dir>`	yes
Resolutions to sweep	`--resolutions 0.4,0.8,1.0,1.4,2.0`	no (default 0.5,0.8,1.0,1.4,2.0)
Cluster methods to use	`--cluster-methods leiden,louvain`	no (default `leiden`)
Explicit member list	`--members leiden:resolution=0.5,louvain:resolution=1.0`	no (overrides the sweep)
Fan-out everything	`--all`	no (sweeps both methods × all default resolutions)
Operator	`--operator {kmode,weighted,lca}`	no (default kmode)
Score weights	`--alpha 0.6 --beta 0.4`	no (ADR 0011 defaults)
Class-imbalance cap	`--max-class-frac 0.8`	no
Pre-run plan confirm	`--confirm-plan`	no
Non-interactive	`--non-interactive`	no
Seed	`--seed 0`	no

Output	Path	Notes
Verified consensus labels	`consensus_labels.tsv`	columns `cell_id,consensus_<operator>`
Per-member labels	`member_<name>/figure_data/embedding_points.csv`	from sc-clustering
Cross-method NMI matrix	`cross_method_nmi.csv`	square per member
Composite scores	`member_scores.csv`	ADR 0011 schema
Markdown report	`report.md`	starts with `[A: Verified consensus]` (non-configurable)
Audit	`plan.json`	resolution sweep + chosen operator + filtered members

Flow

Plan members — either user-supplied (--members / --all) or derived from the resolution sweep × cluster-methods combinations.
Fan out — runtime invokes sc-clustering once per member.
Score — silhouette_score from each member's clustering_summary.csv is the intrinsic-quality signal; cross-method NMI is computed across members.
BC pick — top-K-by-composite-score default; CLI interactive override allowed.
Consensus — kmode / weighted / LCA on the selected base clusterings.
Report — banner + score table + NMI matrix.

Gotchas

--cluster-methods defaults to leiden ONLY, not both, because louvain and leiden agree to within 1–2% on most datasets and the consensus signal comes mostly from the resolution sweep.
Resolutions must span at least one factor of 2 for the consensus to be informative; default sweep covers 0.5–2.0.
The mandatory banner is enforced by runtime/consensus/dispatch.output_banner. Do NOT strip it.
requires_preprocessed: true — run sc-preprocessing first.

Key CLI

# Default sweep (leiden at 5 resolutions)
oc run sc-consensus-clustering --input preprocessed.h5ad --output out/

# Both methods × 5 resolutions = 10 members; SACCELERATOR-style benchmark
oc run sc-consensus-clustering --input preprocessed.h5ad --output out/ \
  --cluster-methods leiden,louvain --resolutions 0.5,0.8,1.0,1.4,2.0

# Explicit
oc run sc-consensus-clustering --input preprocessed.h5ad --output out/ \
  --members leiden:resolution=0.5,leiden:resolution=1.0,louvain:resolution=1.0

Pointers

ADR 0010 — runtime architecture
ADR 0011 — scoring + evaluation
skills/spatial/consensus-domains/ — sibling spatial-side skill