consensus-interpret

name: consensus-interpret description: "LLM-grounded biological interpretation of a verified typed consensus run. Reads the typed run dir + the original adata, runs inline per-cluster DE, looks up markers in a bundled tissue-keyed marker DB, and asks the chair LLM to (γ) name each cluster's likely cell type with mandatory marker citations and (β) recommend top-3 next-step skills with mandatory evidence_refs. Output banner [A+I: Interpreted on verified consensus]. Failure-mode contract per ADR 0012." version: 0.1.0 author: OmicsClaw license: Apache-2.0 tags:

• spatial
• consensus
• interpreted-layer
• biology-annotation
• marker-grounded
• backward-proof-driven-recommendation requires:
• anndata
• scanpy
• numpy
• pandas
• scikit-learn

consensus-interpret

When to use

The user has just finished a verified typed consensus run (consensus-domains or sc-consensus-clustering) and wants the next manual step (read cross_method_nmi.csv → run spatial-de → cross-reference markers → name cell types → decide downstream skill) done automatically with falsifiable evidence binding every LLM claim.

This skill does NOT replace the typed run. It is a strictly downstream consumer: it reads <typed_run_dir>/{plan.json, consensus_labels.tsv, member_scores.csv, cross_method_nmi.csv} plus the original adata, and writes its output to a different directory under analysis://interpreted/<typed_run_id>. The verified-vs-exploratory boundary established by ADR 0010 is preserved.

Skip when:

• The typed run did not produce consensus_labels.tsv (i.e. consensus-domains exited non-zero — fix the typed run first).
• You are looking for a generic query → skill dispatcher; use orchestrator for that (forward direction).
• You want to refine the consensus itself based on LLM judgment; that is explicitly forbidden by §11.4 "LLM never participates in statistical merging" and would be rejected by this skill's T3 invariants.

Inputs & Outputs

interpreted_assignments.json schema

{
  "schema_version": "0.1",
  "typed_run_id": "<plan.json:run_id>",
  "evidence_base_namespace": "analysis://typed/<run_id>",
  "interpreted_namespace": "analysis://interpreted/<run_id>",
  "banner": "[A+I: Interpreted on verified consensus]",
  "operator": "<from typed plan.json>",
  "clusters": [
    {
      "id": 5,
      "n_cells": 778,
      "interpretation_status": "interpreted | low_confidence | failed",
      "cell_type": "CA1 pyramidal",
      "confidence": 0.84,
      "evidence": {
        "markers": [
          {"gene": "Pvrl3", "de_rank": 1, "db_source": "panglaodb_brain", "db_celltype": "CA1 pyramidal neuron", "weight": 0.9},
          {"gene": "Wfs1",  "de_rank": 3, "db_source": "panglaodb_brain", "db_celltype": "CA1 pyramidal neuron", "weight": 0.85}
        ],
        "mean_local_purity": 0.617,
        "member_agreement": [
          {"member": "leiden_resolution-0.5", "label_overlap": 0.92},
          {"member": "leiden_resolution-1.0", "label_overlap": 0.78}
        ]
      },
      "narrative_md_anchor": "#cluster-5"
    }
  ],
  "next_steps": [
    {
      "skill": "spatial-de",
      "args_hint": "--groupby consensus_kmode --comparisons cluster_3_vs_5",
      "priority": 1,
      "evidence_refs": [
        "cross_method_nmi.csv:row=leiden_resolution-0.5,col=leiden_resolution-1.5,value=0.597"
      ],
      "reason": "Lowest pair-wise NMI in matrix; marker disambiguation between clusters 3 and 5 will resolve whether these are sub-types of CA1 or a transition zone."
    }
  ]
}

Flow

1. Preflight (T1 — fail-fast if any fail)
   ├─ Load plan.json from --input; assert schema_version + typed run integrity
   ├─ Locate adata at plan.json.input_path (or --adata override); check exists
   ├─ Load consensus_labels.tsv; assert observation column ⊆ adata.obs.index
   ├─ Resolve marker DB:
   │    --markers <path> if given;
   │    else bundled `data/markers/panglaodb_<tissue>.tsv` for --tissue;
   │    else exit 5 (MarkerDBUnavailable)
   └─ If LLM required and unreachable AND --no-llm not set → exit 6 (LLMUnavailable)

2. Per-cluster differential expression (deterministic, scanpy)
   └─ scanpy.tl.rank_genes_groups(adata, groupby=consensus_<operator>, method='wilcoxon')
       → de_per_cluster.csv with top-K markers per cluster (K=20 default)

3. Marker → cell-type lookup (deterministic, pre-LLM)
   └─ For each cluster, compute candidate cell types by ranking DB entries
       whose gene appears in the cluster's top-K markers (weighted by db.weight × 1/de_rank).

4. LLM grounded interpretation (γ + β; one call per cluster + one synthesis call)
   ├─ Prompt template embeds (per cluster):
   │    cluster_id, n_cells, top-K DE markers,
   │    DB candidate cell types (ranked),
   │    member_agreement summary, cross_method_nmi neighbors
   ├─ LLM must return JSON conforming to interpreted_assignments.json
   │   schema; mandatory evidence.markers[] with non-empty
   │   {gene, db_source, db_celltype}
   └─ After all clusters: one synthesis call to produce next_steps[]
       with mandatory evidence_refs[] (capped at top-3 by priority)

5. Invariant enforcement (T3 — fail-fast if violated)
   ├─ Every cluster.evidence.markers != []      → else exit 7
   ├─ Every next_steps[*].evidence_refs != []   → else exit 7
   └─ Banner present and matches one of two allowed values → else exit 7

6. Coverage check (T2 — escalate to T1 if floor breached)
   └─ interpretable_cluster_frac < --coverage-floor → exit 8

7. Artifact writes
   ├─ interpreted_report.md (banner enforced in format_interpreted_report)
   ├─ interpreted_assignments.json
   ├─ de_per_cluster.csv
   ├─ contradiction_regions.csv
   └─ audit.json

Failure modes (per ADR 0012)

Examples

Default usage (after a typed run completes)

oc run consensus-domains --input preprocessed.h5ad --output run1/ \
  --members banksy,graphst,leiden:resolution=0.5,leiden:resolution=1.0 \
  --non-interactive --operator kmode --seed 0

oc run consensus-interpret --input run1/ --output run1_interpreted/ \
  --tissue brain
# → run1_interpreted/interpreted_report.md begins with [A+I: ...]

CI / offline (structural-only)

oc run consensus-interpret --input run1/ --output run1_struct/ \
  --tissue brain --no-llm
# → run1_struct/interpreted_report.md begins with [I-noLLM: ...]
# → no cell-type claims, only cluster sizes / NMI summary / contradiction regions

User-provided marker DB (non-bundled tissue)

oc run consensus-interpret --input run1/ --output run1_interp/ \
  --markers ~/markers/mouse_intestine.tsv
# → bypasses --tissue requirement; uses user's custom DB

Related skills

References

• ADR 0010 — typed-vs-narrative consensus runtime (boundary integrity)
• ADR 0011 — typed consensus evaluation protocol (composite member score; DLPFC hero)
• ADR 0012 — this skill's evaluation protocol (4-axis panel + T3 invariants)
• docs/CONTEXT.md "Cross-reference: Consensus runtime" — canonical vocabulary