Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
File size: 10,720 Bytes
6d1bbc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 | """Tests for GE omics feature computation module.
Tests gene features, cell line features, omics loading, and combined matrix building.
"""
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from negbiodb_depmap.depmap_db import get_connection, run_ge_migrations
from negbiodb_depmap.ge_features import (
build_feature_matrix,
compute_cell_line_features,
compute_gene_features,
load_omics_features,
)
MIGRATIONS_DIR = Path(__file__).parent.parent / "migrations_depmap"
@pytest.fixture
def tmp_db(tmp_path):
db_path = tmp_path / "test_ge.db"
run_ge_migrations(db_path, MIGRATIONS_DIR)
return db_path
@pytest.fixture
def populated_db(tmp_db):
"""DB with genes, cell lines, screens, negative results, and pairs."""
conn = get_connection(tmp_db)
# Genes
conn.execute(
"INSERT INTO genes (gene_id, entrez_id, gene_symbol, is_common_essential, is_reference_nonessential) "
"VALUES (1, 7157, 'TP53', 0, 0)"
)
conn.execute(
"INSERT INTO genes (gene_id, entrez_id, gene_symbol, is_common_essential, is_reference_nonessential) "
"VALUES (2, 1956, 'EGFR', 0, 1)"
)
conn.execute(
"INSERT INTO genes (gene_id, entrez_id, gene_symbol, is_common_essential, is_reference_nonessential) "
"VALUES (3, 672, 'BRCA1', 1, 0)"
)
# Cell lines
conn.execute(
"INSERT INTO cell_lines (cell_line_id, model_id, ccle_name, lineage, primary_disease) "
"VALUES (1, 'ACH-000001', 'HELA_CERVIX', 'Cervix', 'Cervical Cancer')"
)
conn.execute(
"INSERT INTO cell_lines (cell_line_id, model_id, ccle_name, lineage, primary_disease) "
"VALUES (2, 'ACH-000002', 'MCF7_BREAST', 'Breast', 'Breast Cancer')"
)
conn.execute(
"INSERT INTO cell_lines (cell_line_id, model_id, ccle_name, lineage, primary_disease) "
"VALUES (3, 'ACH-000003', 'A549_LUNG', 'Lung', 'Lung Cancer')"
)
# Screen
conn.execute(
"INSERT INTO ge_screens (screen_id, source_db, depmap_release, screen_type, algorithm) "
"VALUES (1, 'depmap', 'TEST', 'crispr', 'Chronos')"
)
# Negative results
for gid, clid, effect, dep_prob in [
(1, 1, -0.1, 0.1),
(1, 2, -0.2, 0.15),
(2, 1, -0.05, 0.05),
(2, 2, -0.3, 0.2),
(2, 3, -0.15, 0.08),
(3, 1, -0.4, 0.25),
]:
conn.execute(
"INSERT INTO ge_negative_results "
"(gene_id, cell_line_id, screen_id, gene_effect_score, dependency_probability, "
"confidence_tier, evidence_type, source_db, source_record_id, extraction_method) "
"VALUES (?, ?, 1, ?, ?, 'silver', 'crispr_nonessential', 'depmap', 'TEST', 'score_threshold')",
(gid, clid, effect, dep_prob),
)
conn.commit()
# Refresh pairs
from negbiodb_depmap.depmap_db import refresh_all_ge_pairs
refresh_all_ge_pairs(conn)
conn.commit()
conn.close()
return tmp_db
@pytest.fixture
def conn_pop(populated_db):
c = get_connection(populated_db)
yield c
c.close()
# ── Gene features ─────────────────────────────────────────────────────
class TestComputeGeneFeatures:
def test_returns_dataframe(self, conn_pop):
df = compute_gene_features(conn_pop)
assert isinstance(df, pd.DataFrame)
assert len(df) > 0
def test_gene_count(self, conn_pop):
df = compute_gene_features(conn_pop)
assert len(df) == 3 # 3 genes
def test_expected_columns(self, conn_pop):
df = compute_gene_features(conn_pop)
for col in ["mean_effect", "min_effect", "max_effect",
"is_common_essential", "is_reference_nonessential",
"rnai_concordance_fraction"]:
assert col in df.columns
def test_mean_effect_reasonable(self, conn_pop):
df = compute_gene_features(conn_pop)
# Gene 2 (EGFR) has 3 records: -0.05, -0.3, -0.15
row = df.loc[2]
assert -0.5 < row["mean_effect"] < 0.0
def test_common_essential_flag(self, conn_pop):
df = compute_gene_features(conn_pop)
assert df.loc[3, "is_common_essential"] == 1 # BRCA1
assert df.loc[1, "is_common_essential"] == 0 # TP53
# ── Cell line features ────────────────────────────────────────────────
class TestComputeCellLineFeatures:
def test_returns_dataframe(self, conn_pop):
df = compute_cell_line_features(conn_pop)
assert isinstance(df, pd.DataFrame)
assert len(df) > 0
def test_cell_line_count(self, conn_pop):
df = compute_cell_line_features(conn_pop)
assert len(df) == 3 # 3 cell lines
def test_lineage_one_hot(self, conn_pop):
df = compute_cell_line_features(conn_pop)
lineage_cols = [c for c in df.columns if c.startswith("lineage_")]
assert len(lineage_cols) == 3 # Cervix, Breast, Lung
def test_disease_one_hot(self, conn_pop):
df = compute_cell_line_features(conn_pop)
disease_cols = [c for c in df.columns if c.startswith("disease_")]
assert len(disease_cols) == 3
# ── Omics features ───────────────────────────────────────────────────
class TestLoadOmicsFeatures:
def test_empty_when_no_files(self):
result = load_omics_features()
assert result == {}
def test_expression_loading(self, tmp_path):
# Create synthetic expression matrix
data = {"TP53": [5.0, 3.0], "EGFR": [2.0, 4.0]}
df = pd.DataFrame(data, index=["ACH-000001", "ACH-000002"])
expr_file = tmp_path / "expression.csv"
df.to_csv(expr_file, index_label="")
result = load_omics_features(expression_file=expr_file)
assert len(result) > 0
assert ("ACH-000001", "TP53") in result
assert result[("ACH-000001", "TP53")][0] == 5.0 # expression dim
def test_cn_loading(self, tmp_path):
data = {"TP53": [2.0]}
df = pd.DataFrame(data, index=["ACH-000001"])
cn_file = tmp_path / "cn.csv"
df.to_csv(cn_file, index_label="")
result = load_omics_features(cn_file=cn_file)
assert ("ACH-000001", "TP53") in result
assert result[("ACH-000001", "TP53")][1] == 2.0 # CN dim
def test_mutation_loading(self, tmp_path):
data = {"TP53": [1.0], "EGFR": [0.0]}
df = pd.DataFrame(data, index=["ACH-000001"])
mut_file = tmp_path / "mutations.csv"
df.to_csv(mut_file, index_label="")
result = load_omics_features(mutation_file=mut_file)
assert ("ACH-000001", "TP53") in result
assert result[("ACH-000001", "TP53")][2] == 1.0 # mutation dim
def test_combined_3_dims(self, tmp_path):
# All 3 omics files
for fname, data in [
("expr.csv", {"G1": [5.0]}),
("cn.csv", {"G1": [2.0]}),
("mut.csv", {"G1": [1.0]}),
]:
df = pd.DataFrame(data, index=["ACH-000001"])
df.to_csv(tmp_path / fname, index_label="")
result = load_omics_features(
expression_file=tmp_path / "expr.csv",
cn_file=tmp_path / "cn.csv",
mutation_file=tmp_path / "mut.csv",
)
vec = result[("ACH-000001", "G1")]
assert len(vec) == 3
assert vec[0] == 5.0
assert vec[1] == 2.0
assert vec[2] == 1.0
def test_gene_symbol_filter(self, tmp_path):
data = {"TP53": [5.0], "EGFR": [3.0], "BRCA1": [1.0]}
df = pd.DataFrame(data, index=["ACH-000001"])
expr_file = tmp_path / "expression.csv"
df.to_csv(expr_file, index_label="")
result = load_omics_features(
expression_file=expr_file,
gene_symbols=["TP53", "EGFR"],
)
keys = [k[1] for k in result.keys()]
assert "TP53" in keys
assert "EGFR" in keys
def test_model_id_filter(self, tmp_path):
data = {"TP53": [5.0, 3.0]}
df = pd.DataFrame(data, index=["ACH-000001", "ACH-000002"])
expr_file = tmp_path / "expression.csv"
df.to_csv(expr_file, index_label="")
result = load_omics_features(
expression_file=expr_file,
model_ids=["ACH-000001"],
)
model_ids = [k[0] for k in result.keys()]
assert "ACH-000001" in model_ids
assert "ACH-000002" not in model_ids
# ── Build feature matrix ─────────────────────────────────────────────
class TestBuildFeatureMatrix:
def test_basic_matrix(self, conn_pop):
pairs_df = pd.DataFrame({
"gene_id": [1, 2],
"cell_line_id": [1, 2],
"gene_symbol": ["TP53", "EGFR"],
"model_id": ["ACH-000001", "ACH-000002"],
})
X = build_feature_matrix(conn_pop, pairs_df)
assert isinstance(X, np.ndarray)
assert X.shape[0] == 2
assert X.shape[1] > 0
def test_no_nan(self, conn_pop):
pairs_df = pd.DataFrame({
"gene_id": [1],
"cell_line_id": [1],
"gene_symbol": ["TP53"],
"model_id": ["ACH-000001"],
})
X = build_feature_matrix(conn_pop, pairs_df)
assert not np.isnan(X).any()
def test_with_omics(self, conn_pop, tmp_path):
# Create simple omics
data = {"TP53": [5.0]}
df = pd.DataFrame(data, index=["ACH-000001"])
expr_file = tmp_path / "expression.csv"
df.to_csv(expr_file, index_label="")
omics = load_omics_features(expression_file=expr_file)
pairs_df = pd.DataFrame({
"gene_id": [1],
"cell_line_id": [1],
"gene_symbol": ["TP53"],
"model_id": ["ACH-000001"],
})
X = build_feature_matrix(conn_pop, pairs_df, omics_features=omics)
assert X.shape[0] == 1
# Should have gene features + cell line features + 3 omics dims
assert X.shape[1] > 10
def test_unknown_gene_zeros(self, conn_pop):
pairs_df = pd.DataFrame({
"gene_id": [999],
"cell_line_id": [1],
"gene_symbol": ["FAKE"],
"model_id": ["ACH-000001"],
})
X = build_feature_matrix(conn_pop, pairs_df)
assert X.shape[0] == 1
# Gene features should be zero since gene_id 999 doesn't exist
|