python_93040206/基础信息/test5.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# 读入MAF文件
maf_data = pd.read_csv('69dbfe7c-3efc-4126-b36d-2b59aca7a16d.wxs.aliquot_ensemble_masked.maf', sep='\t',  comment='#')

# 提取基因型数据
genotype_data = maf_data.iloc[:, 10:]

# 将基因型转换成0，1，2的数字编码
genotype_data.replace({'AA':0, 'AT':1, 'TT':2}, inplace=True)

# 读入疾病风险相关基因关联数据
risk_gene_data = pd.read_csv('risk_gene_data.csv')

# 将两个数据集根据个体ID进行合并
merged_data = pd.merge(genotype_data, risk_gene_data, on='id')

# 将数据集分为训练集和测试集
train_data, test_data = train_test_split(merged_data, test_size=0.3)

# 构建逻辑回归模型并训练
model = LogisticRegression()
model.fit(train_data.iloc[:, :-1], train_data['risk_score'])

# 对测试集进行预测
test_pred = model.predict(test_data.iloc[:, :-1])

# 计算模型预测准确率
accuracy = np.mean(test_pred == test_data['risk_score'])
print('模型预测准确率：', accuracy)

# 绘制预测结果可视化图表
plt.scatter(test_data['id'], test_data['risk_score'], color='blue', label='True risk score')
plt.scatter(test_data['id'], test_pred, color='red', label='Predicted risk score')
plt.xlabel('Individual ID')
plt.ylabel('Risk score')
plt.title('Genetic risk score prediction')
plt.legend()
plt.show()