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import json
import requests
import numpy as np
import pandas as pd
from scipy.stats import spearmanr
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern, ConstantKernel
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
from sklearn.preprocessing import StandardScaler
import networkx as nx
RESULTS_DIR = "results"
STRING_API = "https://string-db.org/api/json/network"
TAXON_ID = 4932
STRING_SCORE_THRESHOLD = 400
DRUG_EFFLUX_GENES = {
"PDR5", "PDR10", "PDR11", "PDR12", "PDR15", "PDR18", "SNQ2", "YOR1", "YCF1"
}
def load_correlation_df():
path = os.path.join(RESULTS_DIR, "stress_element_nem_correlation.csv")
if not os.path.exists(path):
raise FileNotFoundError(f"{path} not found. Run 03_stress_element_analysis.py first.")
return pd.read_csv(path)
def build_window_features(abc_sequences, nem_results, stress_results,
window_size=100, step_size=50):
from collections import Counter
rows = []
for gene, seqs in abc_sequences.items():
prom = seqs["promoter"]
plen = len(prom)
prom_nem_positions = {n["position"] for n in nem_results.get(gene, {}).get("promoter", [])}
gene_stress = stress_results.get(gene, {})
for start in range(0, plen - window_size, step_size):
window = prom[start:start + window_size]
gc = (window.count("G") + window.count("C")) / window_size
cg = window.count("CG") / (window_size - 1) if window_size > 1 else 0
counts = Counter(window)
entropy = -sum((c / window_size) * np.log2(c / window_size)
for c in counts.values() if c > 0)
homo = max(len(max(window.split(b), key=len)) for b in "ACGT")
rows.append({
"gene": gene, "start": start,
"gc_content": gc, "at_content": 1 - gc, "cg_dinuc": cg,
"entropy": entropy, "homopolymer_max": homo,
"stre_count": sum(1 for e in gene_stress.get("STRE", [])
if start <= e["position"] < start + window_size),
"pdre_count": sum(1 for e in gene_stress.get("PDRE", [])
if start <= e["position"] < start + window_size),
"hse_count": sum(1 for e in gene_stress.get("HSE", [])
if start <= e["position"] < start + window_size),
"ap1_count": sum(1 for e in gene_stress.get("AP1", [])
if start <= e["position"] < start + window_size),
"distance_tss": plen - start,
"nem_count": sum(1 for p in prom_nem_positions
if start <= p < start + window_size),
})
return pd.DataFrame(rows)
def run_random_forest(ml_df):
feature_cols = [
"gc_content", "at_content", "cg_dinuc", "entropy",
"homopolymer_max", "stre_count", "pdre_count",
"hse_count", "ap1_count", "distance_tss",
]
genes = ml_df["gene"].unique()
n_test = max(1, len(genes) // 5)
np.random.seed(42)
test_genes = set(np.random.choice(genes, size=n_test, replace=False))
train_df = ml_df[~ml_df["gene"].isin(test_genes)]
test_df = ml_df[ml_df["gene"].isin(test_genes)]
X_train, y_train = train_df[feature_cols].values, train_df["nem_count"].values
X_test, y_test = test_df[feature_cols].values, test_df["nem_count"].values
rf = RandomForestRegressor(n_estimators=200, random_state=42, n_jobs=-1)
rf.fit(X_train, y_train)
y_pred = rf.predict(X_test)
cv_scores = cross_val_score(rf, ml_df[feature_cols].values,
ml_df["nem_count"].values, cv=5, scoring="r2")
importance_df = pd.DataFrame({
"feature": feature_cols,
"importance": rf.feature_importances_,
}).sort_values("importance", ascending=False)
perf = {
"test_r2": round(float(r2_score(y_test, y_pred)), 3),
"test_rmse": round(float(np.sqrt(mean_squared_error(y_test, y_pred))), 3),
"test_mae": round(float(mean_absolute_error(y_test, y_pred)), 3),
"cv_r2_mean": round(float(cv_scores.mean()), 3),
"cv_r2_std": round(float(cv_scores.std()), 3),
"n_train_windows": int(len(train_df)),
"n_test_windows": int(len(test_df)),
}
return importance_df, perf
def run_gp_landscape(corr_df):
X = corr_df[["PDRE", "total_stress_elements"]].values
y = corr_df["nem_density_per_kb"].values
X_scaled = StandardScaler().fit_transform(X)
gp = GaussianProcessRegressor(
kernel=ConstantKernel(1.0) * Matern(nu=1.5),
n_restarts_optimizer=5, random_state=42
)
gp.fit(X_scaled, y)
y_pred = gp.predict(X_scaled)
return {
"gp_r2": round(float(r2_score(y, y_pred)), 3),
"gp_rmse": round(float(np.sqrt(mean_squared_error(y, y_pred))), 1),
"gp_mae": round(float(mean_absolute_error(y, y_pred)), 1),
}
def fetch_string_interactions(genes):
params = {
"identifiers": "%0d".join(genes),
"species": TAXON_ID,
"required_score": STRING_SCORE_THRESHOLD,
"network_type": "physical",
"caller_identity": "nullomer_study",
}
try:
r = requests.get(STRING_API, params=params, timeout=60)
if r.status_code == 200:
return [
{"gene_a": d["preferredName_A"], "gene_b": d["preferredName_B"], "score": d["score"]}
for d in r.json()
if d["preferredName_A"] in genes and d["preferredName_B"] in genes
]
except Exception:
pass
return []
def build_network(genes, interactions, nem_map):
G = nx.Graph()
for gene in genes:
G.add_node(gene, nem_density=nem_map.get(gene, 0))
for inter in interactions:
if inter["gene_a"] != inter["gene_b"]:
G.add_edge(inter["gene_a"], inter["gene_b"], weight=inter["score"])
return G
def compute_topology(G, nem_map):
degree = dict(G.degree())
betweenness = nx.betweenness_centrality(G)
closeness = nx.closeness_centrality(G)
eigenvector = nx.eigenvector_centrality_numpy(G) if G.number_of_edges() > 0 else {n: 0 for n in G}
rows = []
for node in G.nodes():
rows.append({
"gene": node,
"nem_density": nem_map.get(node, 0),
"degree": degree[node],
"betweenness": betweenness[node],
"closeness": closeness[node],
"eigenvector": eigenvector[node],
"is_drug_efflux": node in DRUG_EFFLUX_GENES,
})
return pd.DataFrame(rows)
def compute_fragility(topo_df):
n = len(topo_df)
max_nem = topo_df["nem_density"].max()
df = topo_df.copy()
df["fragility_score"] = (
0.4 * (df["nem_density"] / max_nem if max_nem > 0 else 0) +
0.3 * (df["degree"] / n) +
0.3 * (df["nem_density"] / max_nem if max_nem > 0 else 0)
)
return df.sort_values("fragility_score", ascending=False)
def main():
corr_df = load_correlation_df()
try:
from importlib.util import spec_from_file_location, module_from_spec
spec2 = spec_from_file_location("nem_mod", "02_nem_analysis.py")
mod2 = module_from_spec(spec2)
spec2.loader.exec_module(mod2)
spec3 = spec_from_file_location("stress_mod", "03_stress_element_analysis.py")
mod3 = module_from_spec(spec3)
spec3.loader.exec_module(mod3)
nullomers = mod2.load_nullomers(os.path.join(RESULTS_DIR, "nullomers_k11.txt"))
gene_coords = mod2.parse_gff(os.path.join("data", "yeast.gff3.gz"))
genome_dict = mod2.load_genome_dict(os.path.join("data", "yeast_genome.fsa"))
abc_sequences = {}
for gene in mod2.ABC_TRANSPORTERS:
if gene in gene_coords:
seqs = mod2.extract_sequences(gene, gene_coords, genome_dict,
mod2.PROMOTER_LENGTH, mod2.DOWNSTREAM_LENGTH)
if seqs:
abc_sequences[gene] = seqs
nem_results = {}
for gene in abc_sequences:
nem_results[gene] = {
"gene": mod2.find_nems(abc_sequences[gene]["gene"], nullomers, mod2.K),
"promoter": mod2.find_nems(abc_sequences[gene]["promoter"], nullomers, mod2.K),
"downstream": mod2.find_nems(abc_sequences[gene]["downstream"], nullomers, mod2.K),
}
stress_results = mod3.scan_stress_elements(abc_sequences)
ml_df = build_window_features(abc_sequences, nem_results, stress_results)
importance_df, perf = run_random_forest(ml_df)
importance_df.to_csv(os.path.join(RESULTS_DIR, "ml_feature_importance.csv"), index=False)
print(f"RF: test R²={perf['test_r2']} RMSE={perf['test_rmse']} "
f"CV R²={perf['cv_r2_mean']}±{perf['cv_r2_std']}")
except Exception as e:
perf = {}
print(f"ML skipped: {e}")
gp_stats = run_gp_landscape(corr_df)
perf.update(gp_stats)
print(f"GP landscape: R²={gp_stats['gp_r2']} RMSE={gp_stats['gp_rmse']}")
with open(os.path.join(RESULTS_DIR, "ml_model_performance.json"), "w") as f:
json.dump(perf, f, indent=2)
genes = corr_df["gene"].tolist()
nem_map = dict(zip(corr_df["gene"], corr_df["nem_density_per_kb"]))
interactions = fetch_string_interactions(genes)
G = build_network(genes, interactions, nem_map)
topo_df = compute_topology(G, nem_map)
frag_df = compute_fragility(topo_df)
topo_df.to_csv(os.path.join(RESULTS_DIR, "network_topology.csv"), index=False)
frag_df.to_csv(os.path.join(RESULTS_DIR, "fragility_scores.csv"), index=False)
for metric in ["degree", "betweenness", "closeness", "eigenvector"]:
rho, p = spearmanr(topo_df[metric], topo_df["nem_density"])
print(f" {metric:12s}: rho={rho:.3f} p={p:.4f}")
# Louvain community detection
try:
import community as community_louvain
except ImportError:
import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "python-louvain"])
import community as community_louvain
partition = community_louvain.best_partition(G, random_state=42)
community_rows = []
community_ids = set(partition.values())
for cid in sorted(community_ids):
members = [g for g, c in partition.items() if c == cid]
mean_nem = float(np.mean([nem_map.get(g, 0) for g in members]))
n_drug_efflux = sum(1 for g in members if g in DRUG_EFFLUX_GENES)
community_rows.append({
"community_id": cid,
"n_genes": len(members),
"genes": ",".join(sorted(members)),
"mean_nem_density": round(mean_nem, 2),
"n_drug_efflux": n_drug_efflux,
})
community_df = pd.DataFrame(community_rows).sort_values("mean_nem_density", ascending=False)
community_df.to_csv(os.path.join(RESULTS_DIR, "network_communities.csv"), index=False)
print(f"Communities: {len(community_ids)} "
f"highest NEM community: {community_df.iloc[0]['mean_nem_density']:.1f} NEMs/kb "
f"({community_df.iloc[0]['n_drug_efflux']} drug efflux genes)")
if __name__ == "__main__":
main()
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