# coding: utf-8
import sys
import numpy as np
import pandas as pd
from sklearn import preprocessing
import networkx as nx
import xlrd
import random
sys.path.append("sideMain2.py")
import sideMain2
df1 = pd.read_csv(r"E:\bigdata\data\du.csv")
def gene_select(gene):
row = int(df1[df1['ge'] == gene].index.values) # 该基因所在行
if row < 100: # 这个基因前或后100个基因中随机挑
g1 = df1.iloc[0:row + 100]
elif row > 17605:
g1 = df1.iloc[row - 100:17705]
else:
g1 = df1.iloc[row - 100:row + 100]
g = int(g1.sample(n=1)['ge']) # 随机选取一个基因数据，并转为int类型
return g
# 数据导入，预处理
edgelist = []
def open_excel(file):
try:
sheet_data = xlrd.open_workbook(file)
return sheet_data
except Exception as e:
print(str(e))
def praise_excel(file):
sheet_data = open_excel(file)
sheet = sheet_data.sheets()[0] #读取sheet
my_queues0 = sheet.col_values(0) #读取1列
my_queues1 = sheet.col_values(1) #读取2列
for i, j in zip(my_queues0,my_queues1):
x = (int(i),int(j))
edgelist.append(x)
praise_excel(r"E:\bigdata\data\Human Interactome_201706.xlsx")
G=nx.Graph() #创建了一个没有节点和边的空图
#列表
G.add_edges_from(edgelist) #对于无向图，边3-2与边2-3被认为是一条边
model = sideMain2.xlsx_file.parse('Gene Vector', index_col='Gene')
def zscore():
df = sideMain2.xlsx_file.parse('00Drug-Gene_Network')
grouped = df.groupby('Drug')
drug = []
target = []
for group in grouped.groups:
drug.append(group)
target.append(list(grouped.get_group(group)['Gene']))
diseaseGene = sideMain2.research_diseaseGene('breast neoplasms')
disease_gene = []
for dG in diseaseGene:
if np.int(dG) in model.iloc[:, 0]:
disease_gene.append(np.int(dG))
closest = []
flg = 0
for item in target: # 2833
print(flg)
flg += 1
closest1 = []
for i in range(1000):
num = len(item)
distance = 0.0
for ge in item: # 30药物
min_dis = 10
if ge in model.iloc[:, 0]:
g1 = gene_select(ge)
else:
num -= 1
if num == 0:
break
else:
continue
for gene in disease_gene:
g2 = gene_select(gene)
# 提取本地基因与基因之间的边数
try:
t = nx.shortest_path_length(G, g1, int(g2))
except nx.NetworkXNoPath:
continue
min_dis = min(min_dis, t)
if min_dis == 10:
num -= 1
else:
distance = distance + min_dis
if num == 0:
closest1.append(np.NAN)
else:
closest1.append(distance / num)
if num < 0:
print("num<0")
closest.append(closest1)
df = pd.DataFrame(closest)
df.to_csv('df_asthma.csv')
if __name__ == "__main__":
zscore()
df = pd.read_csv('df_asthma.csv', index_col=0)
result = pd.DataFrame({'mean': pd.Series(df.mean(axis=1)), 'std': pd.Series(df.std(axis=1))})
# result.to_csv('result_mean_std_asthma.csv')
df1 = pd.read_csv('resultSide_breast neoplasms.csv')
result = pd.concat([df1, result], axis=1)
result.to_csv('6144-8515resultSide_breast neoplasms.csv')
#df2 = pd.read_csv('resultSide_zscore_asthma.csv')
#df2['zscore'] = (df2['distance'] - df2['mean']) / df2['std']
#df2 = df2.sort_values(['zscore']) # 升序
#df2.to_csv('resultSide_zscore_asthma.csv')
