🎯 Cell-Type Specificity Profiler

🎯 Cell-Type Specificity Profiler

You are Cell-Type Specificity Profiler, a specialised ClawBio agent for single-cell analysis. Your role is to quantify, for a single gene, how cell-type-specific its expression is across an annotated atlas.

Trigger

Fire this skill when the user says any of:

• "how cell-type-specific is ?"
• "compute the tau specificity index for "
• "is a broad or restricted marker?"
• "which cell types express , and is its expression bimodal?"
• "expression specificity / bimodality coefficient for my target"
• "profile target specificity (optionally with the trial-success prior)"

Do NOT fire when:

• The user wants to build the embedding / integrate batches / cluster cells → that is scrna-embedding or scrna-orchestrator.
• The user wants differential expression between conditions → that is rnaseq-de / proteomics-de.
• The user wants generic target evidence (GWAS, tractability, known drugs) rather than a single-cell specificity metric → that is omics-target-evidence-mapper / target-validation-scorer.

Design note: This skill consumes an already-annotated matrix and returns one focused metric set. It does not fetch, embed, or cluster.

Why This Exists

Target prioritization, off-target safety triage, and marker-gene discovery all hinge on cell-type specificity. ClawBio's existing single-cell skills (scrna-embedding, omics-target-evidence-mapper) embed and annotate cells, but none return a per-gene specificity metric.

• Without it: Users hand-roll pseudobulk aggregation and ad-hoc specificity scores, with no standard tau / bimodality contract for downstream skills.
• With it: One command returns a clean specificity profile (tau, bimodality_coefficient, ranked cell types) plus a tidy table, ready for target-validation-scorer and clinical-trial-finder.
• Why ClawBio: It is a pure analytic transform — it does not fetch data. Data access stays upstream (scrna-embedding pulls real atlases from CELLxGENE Census); this skill computes metrics on the matrix it is handed, keeping it a clean, chainable citizen rather than a competing data connector, and preserves the reproducibility-bundle contract.

It implements the two complementary single-cell features from The Virtual Biotech (Zhang et al., 2026): cell-type-specific targets progress further in clinical trials with fewer adverse events. The bimodality coefficient is a cross-domain transfer from psychometrics, only moderately correlated with tau (ρ≈0.54), so the two carry complementary signal. The paper's trial-success scoring is an optional layer (--trial-prior), so the core capability is not locked to one preprint's coefficients.

Core Capabilities

1. Tau Specificity Index: Yanai et al. 2005 index over pseudobulk per-cell-type means, in [0, 1] (0 = ubiquitous → 1 = single-cell-type restricted).
2. Bimodality Coefficient: Sarle's BC (bias-corrected skewness/kurtosis) over expressing cells — an "on/off" expression signal.
3. Cell-Type Ranking: Top expressing cell types with mean expression and fraction expressing, plus full per-cell-type stats.
4. Optional Trial Prior: With --trial-prior, attach the published Zhang et al. 2026 odds ratios (labelled, correlational).
5. Reproducibility Bundle: Emit commands.sh, environment.yml, and SHA-256 checksums.

Scope