1. 构建随机互作网络
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 20 10:47:00 2018
@author: xllix
"""
#coding=gbk
#python -m pip install --upgrade pip 更新pip
import networkx
import random
import os
### String_human_PPIs作为背景集进行crosstalk分析
### 用python随机1000次，得到1000个随机网络 randomRRI1-1000
f_PPIs = open(r'C:\\Users\\xllix\\Documents\\9-Symbol_900_si.txt') #900分
random_path = 'C:\\Users\\xllix\\Documents\\9-randomNet'
error_path = 'C:\\Users\\xllix\\Documents\\9-randomError'
originalNet = networkx.Graph() #原始无向网络 空图
allGene = originalNet.nodes() #节点
netDegree = originalNet.degree() #度
for line in f_PPIs.readlines():
gene = line.strip().split()
if gene[0] != gene[1]:
originalNet.add_edge(gene[0], gene[1]) #边
f_PPIs.close()
print (originalNet.number_of_nodes(), originalNet.number_of_edges())
print ('---------------infor of original net---------------')
print ('---------------infor of random PPIs----------------')
for i in range(1,1001):
randomPPIsname = 'randomPPIs'+str(i)
errorName = 'error' + str(i)
randomPPIs = networkx.Graph()
existedNode = []
f_error_out = open(os.path.join(error_path, errorName+'.txt'), 'w')
allGene = random.sample(allGene, len(allGene))
for g1 in allGene:
existedNode.append(g1)
randomPPIs.add_node(g1)
existedNode1 = randomPPIs.neighbors(g1)
g1_degree = randomPPIs.degree(g1)
remainGene = list(set(allGene).difference(set(existedNode)).difference(set(existedNode1)))
try:
randGene = random.sample(remainGene, netDegree[g1]-g1_degree)
except:
randGene = random.sample(remainGene, len(remainGene))
f_error_out.write(g1 + '\t' + str(originalNet.degree(g1)) + '\t' + str(randomPPIs.degree(g1) + len(remainGene)) + '\n')
for g2 in randGene:
randomPPIs.add_edge(g1, g2)
if randomPPIs.degree(g2) == netDegree[g2]:
existedNode.append(g2)
f_error_out.close()
print (i,randomPPIs.number_of_nodes(), randomPPIs.number_of_edges())
f_random_out = open(os.path.join(random_path, randomPPIsname+'.txt'), 'w')
for p1 in randomPPIs.nodes():
for p2 in randomPPIs.neighbors(p1):
pair1 = p1 + '\t' + p2
f_random_out.write(pair1 + '\n')
f_random_out.close()
2. 用python寻找交互关系
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 20 21:44:33 2018
@author: xllix
"""
### 用python寻找crosstalk交互关系
import os
f_module = open(r'C:\\Users\\xllix\\Documents\\test\\Random_set_m2m4_2.txt') #同一模块的基因在一行
f_crosstalk = open(r'C:\\Users\\xllix\\Documents\\Crosstalk_Result.txt', 'w')
f_realNet = 'C:\\Users\\xllix\\Documents\\9-Symbol_900_si.txt'
path_randNet = 'C:\\Users\\xllix\\Documents\\9-randomNet'
module1 = {}
data_module = f_module.readlines()
f_module.close()
nModule = len(data_module)
for index, l_module in enumerate(data_module):#enumerate用于一个可遍历的数据对象，同时列出数据和数据下标
#print(index,l_module)
l_m = l_module.strip().split()
moduleName = 'm' + str(index+1)
print(moduleName)
module1[moduleName] = l_m #{}字典，冒号':'分开键和值，逗号','隔开组。[]可变列表
print(l_m)
f_module.close()
print ('---------------infor of module gene---------------')
print ('---------------infor of module crosstalk----------')
def calInteractionNu(m1, m2, net):
f_net = open(net)
countCross = 0
L=[]
R=[]
for l_net in f_net.readlines():
pair = l_net.strip().split()
if pair[0] in m1 and pair[1] in m2:
countCross += 1
L.append(pair)
f_net.close()
R.append(countCross)
R.append(L)
return (R)
module2 = module1
f_crosstalk.write('module1 module2 num_of_crosstalk p_value crosstalk\n')
print ("'''自定义函数（找互作次数）'''")
done = []
for m1k, m1v in module1.items():
for m2k, m2v in module2.items():
pairModule1 = m1k+'\t'+m2k
pairModule2 = m2k+'\t'+m1k
if pairModule1 not in done and pairModule2 not in done:
done.append(pairModule1)
if m1k != m2k:
nRealPair = calInteractionNu(m1v, m2v, f_realNet)[0]
crosstalk = calInteractionNu(m1v, m2v, f_realNet)[1]
if nRealPair > 0:
for root, dirs, randFiles in os.walk(path_randNet):
countLagerRand = 0
for randF in randFiles:
f_randNet = os.path.join(path_randNet, randF)
nRandPair = calInteractionNu(m1v, m2v, f_randNet)[0]
if nRealPair < nRandPair:
countLagerRand += 1
p_value = float(countLagerRand)/len(randFiles)
print(float(countLagerRand))
f_crosstalk.write(m1k+'\t'+m2k+'\t'+str(nRealPair)+'\t'+str(p_value)+'\t'+str(crosstalk)+'\n')
f_crosstalk.close()
3. 筛选显著crosstalk
### 用python筛选显著crosstalk
f_crosstalk = open(r'E:\\ASD-自噬毕业课题\\开学前\\821\\Crosstalk_Result.txt')
f_out = open(r'E:\\ASD-自噬毕业课题\\开学前\\821\\Crosstalk_significant_module.txt', 'w')
f_out.write('module1 module2 num_of_crosstalk p_value\n')
f_crosstalk.readline()
for line in f_crosstalk.readlines():
l = line.strip().split()
if float(l[3]) < 0.05:
f_out.write(line)
f_crosstalk.close()
f_out.close()
4. 以比较互作次数为主
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 19 17:57:15 2018
@author: xllix
"""
import os
f_realNet = 'C:\\Users\\xllix\\Documents\\crosstalk_possess\\9-Symbol_900_si.txt' #真实PPI
f_crosstalk = open(r'C:\\Users\\xllix\\Documents\\3\\Crosstalk_between_modules.txt', 'a+')
#f_crosstalk = open(r'C:\\Users\\xllix\\Documents\\Rand_sets\\Crosstalk_Results_rand_m4m2.txt', 'a+') #随机
#f_crosstalk = open(r'C:\\Users\\xllix\\Documents\\Real_sets\\Crosstalk_Results_real.txt', 'a+') #真实
path="C:\\Users\\xllix\\Documents\\3"
#path="C:\\Users\\xllix\\Documents\\Rand_sets\\rand_set_m4m2\\" #随机
#path="C:\\Users\\xllix\\Documents\\Real_sets\\" #真实
path_list=os.listdir(path)
print(len(path_list))
f_crosstalk.write('from to files num_of_crosstalk crosstalk\n')
for files in path_list:
print(os.path.join(path,files))
f_module= open(r'C:\\Users\\xllix\\Documents\\3\\'+files)
#f_module= open(r'C:\\Users\\xllix\\Documents\\Rand_sets\\rand_set_m4m2\\'+files) #随机
#f_module= open(r'C:\\Users\\xllix\\Documents\\Real_sets\\'+files) #真实
module1 = {}
data_module = f_module.readlines()
f_module.close()
nModule = len(data_module)
print ('---------------infor of module gene---------------')
for index, l_module in enumerate(data_module):#enumerate用于一个可遍历的数据对象，同时列出数据和数据下标
#print(index,l_module)
l_m = l_module.strip().split()
moduleName = 'm' + str(index+1)
print(moduleName)
module1[moduleName] = l_m #{}字典，冒号':'分开键和值，逗号','隔开组。[]可变列表
print(l_m)
f_module.close()
print ('---------------infor of module crosstalk----------')
def calInteractionNu(m1, m2, net):
f_net = open(net)
countCross = 0
L=[]
R=[]
for l_net in f_net.readlines():
pair = l_net.strip().split()
if pair[0] in m1 and pair[1] in m2:
countCross += 1
L.append(pair)
f_net.close()
R.append(countCross)
R.append(L)
return (R)
module2 = module1
print ("'''自定义函数（找互作次数）'''")
done = []
for m1k, m1v in module1.items():
for m2k, m2v in module2.items():
pairModule1 = m1k+'\t'+m2k
pairModule2 = m2k+'\t'+m1k
if pairModule1 not in done and pairModule2 not in done:
done.append(pairModule1)
if m1k != m2k:
nRealPair = calInteractionNu(m1v, m2v, f_realNet)[0]
print(nRealPair)
crosstalk = calInteractionNu(m1v, m2v, f_realNet)[1]
f_crosstalk.write(m1k+'\t'+m2k+'\t'+str(files)+'\t'+str(nRealPair)+'\t'+str(crosstalk)+'\n')
#f_crosstalk.write(m1k+'\t'+m2k+'\t'+str(nRealPair)+'\t'+str(crosstalk)+'\n')
#if nRealPair > 78:
# print('yeah！！')
# f_crosstalk.write(str(files)+'\t'+str(nRealPair)+'\t'+str(crosstalk)+'\n')
f_crosstalk.close()
