feat(03-03): implement protein evidence layer with UniProt/InterPro integration

- Create protein features data model with domain, coiled-coil, TM, cilia motifs
- Implement fetch.py with UniProt REST API and InterPro API queries
- Implement transform.py with feature extraction, motif detection, normalization
- Implement load.py with DuckDB persistence and provenance tracking
- Add CLI protein command following evidence layer pattern
- Add comprehensive unit and integration tests (all passing)
- Handle NULL preservation and List(Null) edge case
- Add get_steps() method to ProvenanceTracker for test compatibility

tests/test_expression.py

@@ -0,0 +1,167 @@
+"""Unit tests for expression evidence layer.
+
+Tests tau calculation, enrichment scoring, and null handling with synthetic data.
+NO external API calls - all data is mocked or synthetic.
+"""
+
+import polars as pl
+import pytest
+
+from usher_pipeline.evidence.expression.transform import (
+    calculate_tau_specificity,
+    compute_expression_score,
+)
+
+
+def test_tau_calculation_ubiquitous():
+    """Equal expression across tissues -> Tau near 0 (ubiquitous)."""
+    # Create synthetic data with equal expression across tissues
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001", "ENSG00000002"],
+        "tissue1": [10.0, 20.0],
+        "tissue2": [10.0, 20.0],
+        "tissue3": [10.0, 20.0],
+        "tissue4": [10.0, 20.0],
+    })
+
+    tissue_cols = ["tissue1", "tissue2", "tissue3", "tissue4"]
+    result = calculate_tau_specificity(df, tissue_cols)
+
+    # Tau should be close to 0 for ubiquitous expression
+    assert "tau_specificity" in result.columns
+    tau_values = result.select("tau_specificity").to_series().to_list()
+    assert tau_values[0] == pytest.approx(0.0, abs=0.01)
+    assert tau_values[1] == pytest.approx(0.0, abs=0.01)
+
+
+def test_tau_calculation_specific():
+    """Expression in one tissue only -> Tau near 1 (tissue-specific)."""
+    # Gene expressed only in one tissue
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001"],
+        "tissue1": [100.0],
+        "tissue2": [0.0],
+        "tissue3": [0.0],
+        "tissue4": [0.0],
+    })
+
+    tissue_cols = ["tissue1", "tissue2", "tissue3", "tissue4"]
+    result = calculate_tau_specificity(df, tissue_cols)
+
+    tau = result.select("tau_specificity").item()
+    # Tau = sum(1 - xi/xmax) / (n-1) = (0 + 1 + 1 + 1) / 3 = 1.0
+    assert tau == pytest.approx(1.0, abs=0.01)
+
+
+def test_tau_null_handling():
+    """NULL tissue values -> NULL Tau (insufficient data)."""
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001", "ENSG00000002"],
+        "tissue1": [10.0, 20.0],
+        "tissue2": [None, 20.0],  # NULL for gene 1
+        "tissue3": [10.0, 20.0],
+        "tissue4": [10.0, 20.0],
+    })
+
+    tissue_cols = ["tissue1", "tissue2", "tissue3", "tissue4"]
+    result = calculate_tau_specificity(df, tissue_cols)
+
+    tau_values = result.select("tau_specificity").to_series().to_list()
+    # Gene 1 has NULL tissue -> NULL Tau
+    assert tau_values[0] is None
+    # Gene 2 has complete data -> Tau should be valid
+    assert tau_values[1] is not None
+
+
+def test_enrichment_score_high():
+    """High retina expression relative to global -> high enrichment."""
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001"],
+        "hpa_retina_tpm": [50.0],
+        "hpa_cerebellum_tpm": [40.0],
+        "gtex_retina_tpm": [60.0],
+        "hpa_testis_tpm": [5.0],
+        "hpa_fallopian_tube_tpm": [5.0],
+        "gtex_testis_tpm": [5.0],
+        "cellxgene_photoreceptor_expr": [None],
+        "cellxgene_hair_cell_expr": [None],
+        "tau_specificity": [0.5],
+    })
+
+    result = compute_expression_score(df)
+
+    # Usher tissues (retina, cerebellum) have much higher expression than global
+    # Mean Usher: (50+40+60)/3 = 50
+    # Mean global: (50+40+60+5+5+5)/6 = 27.5
+    # Enrichment: 50/27.5 ≈ 1.82
+    assert "usher_tissue_enrichment" in result.columns
+    enrichment = result.select("usher_tissue_enrichment").item()
+    assert enrichment > 1.5  # Significantly enriched
+
+
+def test_enrichment_score_low():
+    """No target tissue expression -> low enrichment."""
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001"],
+        "hpa_retina_tpm": [5.0],
+        "hpa_cerebellum_tpm": [5.0],
+        "gtex_retina_tpm": [5.0],
+        "hpa_testis_tpm": [50.0],
+        "hpa_fallopian_tube_tpm": [50.0],
+        "gtex_testis_tpm": [50.0],
+        "cellxgene_photoreceptor_expr": [None],
+        "cellxgene_hair_cell_expr": [None],
+        "tau_specificity": [0.8],
+    })
+
+    result = compute_expression_score(df)
+
+    enrichment = result.select("usher_tissue_enrichment").item()
+    assert enrichment < 1.0  # Not enriched in Usher tissues
+
+
+def test_expression_score_normalization():
+    """Composite score should be in [0, 1] range."""
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001", "ENSG00000002", "ENSG00000003"],
+        "hpa_retina_tpm": [50.0, 10.0, 5.0],
+        "hpa_cerebellum_tpm": [40.0, 10.0, 5.0],
+        "gtex_retina_tpm": [60.0, 10.0, 5.0],
+        "hpa_testis_tpm": [5.0, 50.0, 50.0],
+        "hpa_fallopian_tube_tpm": [5.0, 50.0, 50.0],
+        "gtex_testis_tpm": [5.0, 50.0, 50.0],
+        "cellxgene_photoreceptor_expr": [None, None, None],
+        "cellxgene_hair_cell_expr": [None, None, None],
+        "tau_specificity": [0.5, 0.3, 0.2],
+    })
+
+    result = compute_expression_score(df)
+
+    scores = result.select("expression_score_normalized").to_series().to_list()
+    for score in scores:
+        if score is not None:
+            assert 0.0 <= score <= 1.0, f"Score {score} out of range [0,1]"
+
+
+def test_null_preservation_all_sources():
+    """Gene with no data from any source -> NULL score."""
+    df = pl.DataFrame({
+        "gene_id": ["ENSG00000001"],
+        "hpa_retina_tpm": [None],
+        "hpa_cerebellum_tpm": [None],
+        "gtex_retina_tpm": [None],
+        "hpa_testis_tpm": [None],
+        "hpa_fallopian_tube_tpm": [None],
+        "gtex_testis_tpm": [None],
+        "cellxgene_photoreceptor_expr": [None],
+        "cellxgene_hair_cell_expr": [None],
+        "tau_specificity": [None],
+    })
+
+    result = compute_expression_score(df)
+
+    # Both enrichment and score should be NULL
+    enrichment = result.select("usher_tissue_enrichment").item()
+    score = result.select("expression_score_normalized").item()
+    assert enrichment is None
+    assert score is None