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import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..')))
import traceback
from sklearn.linear_model import LogisticRegression, LinearRegression
from tools.statistics import *
from utils.utils import get_question_pairs
task_info_file = '../metadata/task_info.json'
all_pairs = get_question_pairs(task_info_file)
in_data_root = '../output/preprocess'
output_root = '../output/regress'
for i, (trait, condition) in enumerate(all_pairs):
print(f"Analyzing question {i}: trait {trait} and condition {condition}")
try:
if condition is None:
print(f"Trait {trait} only")
trait_data, _, _ = select_and_load_cohort(in_data_root, trait, is_two_step=False)
trait_data = trait_data.drop(columns=['Age', 'Gender'], errors="ignore")
Y = trait_data[trait].values
X = trait_data.drop(columns=[trait]).values
has_batch_effect = detect_batch_effect(X)
if has_batch_effect:
model_constructor = LMM
else:
model_constructor = Lasso
param_values = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1]
best_config, best_performance = tune_hyperparameters(model_constructor, param_values, X, Y,
trait_data.columns, trait, task_info_file,
condition)
model = ResidualizationRegressor(model_constructor, best_config)
normalized_X, _ = normalize_data(X)
model.fit(normalized_X, Y)
var_names = trait_data.columns.tolist()
significant_genes = interpret_result(model, var_names, trait, condition)
save_result(significant_genes, best_performance, output_root, trait)
else:
if condition in ['Age', 'Gender']:
trait_data, _, _ = select_and_load_cohort(in_data_root, trait, condition, is_two_step=False)
redundant_col = 'Age' if condition == 'Gender' else 'Gender'
if redundant_col in trait_data.columns:
trait_data = trait_data.drop(columns=[redundant_col])
else:
trait_data, condition_data, regressors = select_and_load_cohort(in_data_root, trait, condition, is_two_step=True, gene_info_path=task_info_file)
trait_data = trait_data.drop(columns=['Age', 'Gender'], errors='ignore')
if regressors is None:
print(f'No gene regressors for trait {trait} and condition {condition}')
continue
print("Common gene regressors for condition and trait", regressors)
X_condition = condition_data[regressors].values
Y_condition = condition_data[condition].values
condition_type = 'binary' if len(np.unique(Y_condition)) == 2 else 'continuous'
if condition_type == 'binary':
if X_condition.shape[1] > X_condition.shape[0]:
model = LogisticRegression(penalty='l1', solver='liblinear', random_state=42)
else:
model = LogisticRegression()
else:
if X_condition.shape[1] > X_condition.shape[0]:
model = Lasso()
else:
model = LinearRegression()
normalized_X_condition, _ = normalize_data(X_condition)
model.fit(normalized_X_condition, Y_condition)
regressors_in_trait = trait_data[regressors].values
normalized_regressors_in_trait, _ = normalize_data(regressors_in_trait)
if condition_type == 'binary':
predicted_condition = model.predict_proba(normalized_regressors_in_trait)[:, 1]
else:
predicted_condition = model.predict(normalized_regressors_in_trait)
trait_data[condition] = predicted_condition
Y = trait_data[trait].values
Z = trait_data[condition].values
X = trait_data.drop(columns=[trait, condition]).values
has_batch_effect = detect_batch_effect(X)
if has_batch_effect:
model_constructor = LMM
else:
model_constructor = Lasso
param_values = [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1]
best_config, best_performance = tune_hyperparameters(model_constructor, param_values, X, Y, trait_data.columns, trait, task_info_file, condition, Z)
model = ResidualizationRegressor(model_constructor, best_config)
normalized_X, _ = normalize_data(X)
normalized_Z, _ = normalize_data(Z)
model.fit(normalized_X, Y, normalized_Z)
var_names = trait_data.columns.tolist()
significant_genes = interpret_result(model, var_names, trait, condition)
save_result(significant_genes, best_performance, output_root, trait, condition)
except Exception as e:
print(f"Error processing pair {i}, for the trait '{trait}' and the condition '{condition}':\n{traceback.format_exc()}")
continue |