--- name: clawbio/celltype-specificity-profiler source: https://app.decimal.ai/s/clawbio-celltype-specificity-profiler@1/SKILL.md source_sha256: f1d2452a9034 --- # 🎯 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 **One skill, one task.** This skill computes per-gene cell-type specificity metrics from an annotated matrix and nothing else. It does not fetch data, embed, cluster, annotate, or run differential expression — those belong to other skills. ## Input Formats | Format | Extension | Required Fields | Example | |--------|-----------|-----------------|---------| | AnnData annotated matrix | `.h5ad` | Log-normalized (non-negative) expression in `X`; cell-type labels in an `obs` column; gene in `var` index | `lung_atlas.h5ad` | | Demo mode | n/a | none — uses scanpy's bundled, real `pbmc3k` dataset | `--demo` | In the chain, the `.h5ad` is the **output of upstream `scrna-embedding`**, not fetched here. ## Workflow 1. **Load**: Read the `.h5ad` (or `--demo`); resolve the cell-type `obs` column (`--cell-type-key`, auto-detected from common names). 2. **Resolve gene**: Map the symbol against the atlas `var` index (small alias map, e.g. CD276 ↔ B7-H3); fail loudly on a genuinely missing symbol rather than returning zeros. *(Prescriptive.)* 3. **Subset**: If `--tissue` is given, restrict to that label; error if absent. *(Prescriptive.)* 4. **Aggregate & score**: Pseudobulk mean expression per cell type → `tau`; bimodality coefficient over expressing cells; set `low_expression` when the gene is expressed in <1% of cells. *(Prescriptive.)* 5. **Generate**: Write `profile.json`, `per_celltype.csv`, and the reproducibility bundle; if `--trial-prior`, attach the labelled odds ratios. *(Prescriptive.)* ## CLI Reference ```bash # Standard usage — profile a gene against your own atlas python skills/celltype-specificity-profiler/profiler.py \ --gene CD276 --atlas lung_atlas.h5ad --output # Restrict to a tissue and attach the paper's trial-success prior python skills/celltype-specificity-profiler/profiler.py \ --gene CD276 --atlas lung_atlas.h5ad --tissue lung --trial-prior --output # Demo mode (real scanpy-bundled pbmc3k; default gene MS4A1) python skills/celltype-specificity-profiler/profiler.py --demo --output # Via ClawBio runner python clawbio.py run celltype-specificity-profiler --demo ``` ## Demo ```bash python clawbio.py run celltype-specificity-profiler --demo ``` The demo runs on scanpy's bundled, **real** `pbmc3k` 10x dataset (2,638 cells, annotated cell types) — no synthetic data. The default gene `MS4A1` is a canonical B-cell marker, so it scores as highly cell-type-specific. A reference of this output ships at `examples/expected_demo_profile.json`. ## Algorithm / Methodology 1. Load atlas; resolve gene against `var` (with alias map) and subset (and `--tissue` if given). 2. Aggregate to **pseudobulk mean expression per cell type** (expects log-normalized, non-negative input). 3. **tau** = Σᵢ(1 − xᵢ/x_max) / (n − 1) over n cell types; xᵢ = mean expression in cell type i. NaN for n < 2. Following Zhang et al. 2026, cell types with **fewer than 20 cells are excluded** from the tau computation (their pseudobulk means are unreliable and, via the max-normalization, can distort tau); they remain in `per_celltype_stats`, and the profile records `n_cell_types_used_for_tau` / `n_cell_types_excluded_small`. 4. **Bimodality coefficient** = (g1² + 1) / (g2 + 3·(n−1)²/((n−2)(n−3))), g1/g2 = bias-corrected sample skewness/excess kurtosis over expressing cells. NaN for n < 4 or zero variance. 5. Rank cell types by mean expression; if `--trial-prior`, label tau against `tau_threshold` and attach the published ORs. **Key thresholds / parameters**: - `TAU_THRESHOLD = 0.69` — tau > 0.69 → "cell-type-specific". This is **not** a universal constant: Zhang et al. 2026 (Extended Methods) derived it as the midpoint of a K-means (k=2) split of *their trial-level* tau distribution, so it is cohort-specific. Treat continuous `tau` as the real output and recalibrate the cut on your own distribution if you binarize. - `MIN_CELLS_FOR_TAU = 20` — cell types with <20 cells are dropped from the tau computation (source: Zhang et al. 2026). - `LOW_EXPRESSION_FRACTION = 0.01` — gene expressed in <1% of cells flags an unreliable BC. - Trial-prior odds ratios: phase I→II OR 1.27 (95% CI 1.22–1.33), primary-endpoint OR 1.11 (95% CI 1.09–1.14) — verified verbatim against Zhang et al. 2026 Results. ## Example Queries - "How cell-type-specific is CD276 in this lung atlas?" - "Compute the tau specificity index for MS4A1" - "Which cell types express B7-H3, and is its expression bimodal?" - "Profile this gene's specificity and give me the trial-success prior" ## Example Output `profile.json` (demo, `--demo --trial-prior`, abbreviated): ```json { "skill": "celltype-specificity-profiler", "gene": "MS4A1", "atlas": "pbmc3k (10x, real; scanpy bundled)", "tau": 0.956, "tau_threshold": 0.69, "tau_threshold_note": "cohort-specific K-means(k=2) midpoint of the trial-level tau distribution in Zhang et al. 2026 (tau=0.69); an interpretive default, not a universal cutoff", "n_cell_types_used_for_tau": 7, "n_cell_types_excluded_small": 1, "bimodality_coefficient": 0.4936, "interpretation": "cell-type-specific (tau > 0.69)", "low_expression": false, "top_cell_types": [ {"cell_type": "B cells", "mean_expr": 0.993, "pct_expressing": 0.8596}, {"cell_type": "FCGR3A+ Monocytes", "mean_expr": 0.0601, "pct_expressing": 0.0867} ], "trial_prior": { "note": "Odds ratios from Zhang et al. 2026 (bioRxiv 10.64898/2026.02.23.707551)", "phase_I_to_II_OR": 1.27, "primary_endpoint_OR": 1.11, "lower_AE_rate": true } } ``` `per_celltype.csv`: ```csv cell_type,mean_expr,median_expr,pct_expressing,n_cells B cells,0.993,1.0986,0.8596,342 FCGR3A+ Monocytes,0.0601,0.0,0.0867,150 ``` *ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses.* ## Output Structure ```text output_directory/ ├── profile.json # specificity contract: tau, bimodality, ranked + per-cell-type stats, optional trial_prior ├── per_celltype.csv # tidy per-cell-type table └── reproducibility/ ├── commands.sh # exact command to reproduce ├── environment.yml # pip/conda environment snapshot └── checksums.sha256 # SHA-256 of the outputs ``` ## Dependencies **Required**: - `scanpy`; load atlas / bundled demo dataset - `anndata` >= 0.9; `.h5ad` I/O - `numpy` >= 1.23; tau / bimodality math - `scipy` >= 1.9; distribution statistics - `pandas` >= 2.0; tabular output No network access required in `--demo` mode after pbmc3k is cached on first fetch. ## Gotchas - **Sparse genes**: The model will want to trust the bimodality coefficient for any gene. Do not — a gene expressed in <1% of cells gives an unstable BC; the skill sets `low_expression: true` and the BC must be treated as unreliable. - **Annotation granularity drives tau**: The model will want to compare tau across atlases. Do not — coarse labels ("immune cell") inflate apparent ubiquity, fine labels raise tau. Always report the annotation level; never compare tau across atlases with different ontologies. - **The 0.69 threshold is cohort-specific**: The model will want to treat `tau > 0.69` as an absolute "specific" verdict. Do not — Zhang et al. 2026 obtained 0.69 from K-means (k=2) on *their* trial-level tau distribution, so the binary call is an interpretive convenience. Report the continuous `tau`, and recalibrate the cut on your own distribution if you must binarize. - **Per-atlas tau ≠ the paper's per-gene tau**: The model will want to compare a single run against the paper's published per-gene values. Do not — this skill returns tau *within the one matrix it is handed*, whereas Zhang et al. 2026 average per-tissue tau across all tissues where the gene is expressed (a job for the orchestrator calling this skill per tissue). - **Thin atlases under-power tau**: The model will want to trust tau from a small slice. Do not — cell types with <20 cells are dropped from the tau computation, so on a thin atlas several groups may be excluded; check `n_cell_types_used_for_tau` first. - **Log-normalized input required**: The model will want to feed whatever `X` is present. Do not — tau assumes non-negative expression. Z-scored matrices (with negatives) produce meaningless tau; the demo deliberately reads `.raw` (log-normalized) rather than the z-scored `.X`. - **Symbol/Ensembl mismatch**: The model will want to return zeros when a symbol is absent. Do not — the gene must resolve in `var`; a small alias map handles CD276 ↔ B7-H3, but novel/retired symbols fail loudly. - **`--trial-prior` is correlational**: The model will want to present odds ratios as predictive. Do not — they come from one observational study and are not a guarantee of trial success. ## Safety - **Local-first**: Pure local computation on a provided matrix; no data upload. - **Disclaimer**: *ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.* - **Audit trail**: Writes a `commands.sh` / `environment.yml` / `checksums.sha256` reproducibility bundle for every run. - **No hallucinated science**: tau, bimodality, thresholds, and odds ratios all trace to cited sources; missing genes/columns raise rather than returning silent zeros. ## Agent Boundary The agent (LLM) dispatches this skill and explains its output. The skill (Python) executes the computation. The agent must NOT recompute tau/bimodality by hand, override the thresholds, invent cell types, or present the `--trial-prior` odds ratios as causal. ## Chaining Partners - **Upstream** — `scrna-embedding` / `scrna-orchestrator`: produce the annotated `.h5ad` this skill consumes; `omics-target-evidence-mapper`: supplies the candidate gene. - **Downstream** — `target-validation-scorer`: ingests `profile.json`'s specificity features; `clinical-trial-finder`: uses the prioritized target. Chain: `omics-target-evidence-mapper` → **`celltype-specificity-profiler`** → `target-validation-scorer` → `clinical-trial-finder`. - Output is structured JSON/CSV, so it chains cleanly via the Bio Orchestrator. ## Maintenance - **Review cadence**: Re-check on each major `scanpy`/`anndata` release (the demo loader uses `sc.datasets.pbmc3k_processed`). - **Staleness signals**: scanpy changes the bundled `pbmc3k` API or `.raw` layout; HGNC retires an aliased symbol; the Zhang et al. odds ratios are superseded by a peer-reviewed version. - **Deprecation criteria**: Retire if ClawBio adds a first-class per-gene specificity metric to `scrna-orchestrator`, or fold the trial-prior block into a dedicated scoring skill. ## Citations - Zhang H.G., Eckmann P., Miao J., Mahon A.B., Zou J. *The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and Development.* bioRxiv 2026. doi:10.64898/2026.02.23.707551 - Yanai I. et al. *Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification* (tau specificity index). Bioinformatics 2005. - Pfister R., Schwarz K.A., Janczyk M., Dale R., Freeman J.B. *Good things peak in pairs: a note on the bimodality coefficient.* Frontiers in Psychology 2013. - Tabula Sapiens Consortium. *Tabula Sapiens v2.* CZ CELLxGENE Census.