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ncm_test.py
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ncm_test.py
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import os
import math
import subprocess, time
import argparse
from argparse import RawTextHelpFormatter
from subprocess import call
global bed_file
global outdir
global outfilename
global temp_out
global testsamplename
global SAMTOOLS
global BCFTOOLS
global REF
global bam_list
glob_scores = dict() #Whole score
feature_list = dict() #Each Feature List
label = [] #Samples
features = [] #dbSNP features
mean_depth = dict()
sum_file = dict()
out_tag = ""
pdf_tag = ""
Family_flag = False
#Calculation of AVerages
def average(x):
assert len(x) > 0
return float(sum(x)) / len(x)
#Calulation of Pearson Correlation
def pearson_def(x, y):
assert len(x) == len(y)
n = len(x)
## Need to be checked , n==0 case
if n == 0 :
return 0
assert n > 0
avg_x = average(x)
avg_y = average(y)
diffprod = 0
xdiff2 = 0
ydiff2 = 0
for idx in range(n):
xdiff = x[idx] - avg_x
ydiff = y[idx] - avg_y
diffprod += xdiff * ydiff
xdiff2 += xdiff * xdiff
ydiff2 += ydiff * ydiff
return diffprod / math.sqrt(xdiff2 * ydiff2)
# createDataSet
# base_dir : directory of files, bedFile: name of the bedFile
def createDataSetFromDir(base_dir, bedFile):
for root, dirs, files in os.walk(base_dir):
for file in files:
if not file.endswith(".vcf"):
continue
link = root + '/' + file
f = open(link, "r")
dbsnpf= open(bedFile,"r")
depth = 0
count = 0
sum = 0
scores = dict() # Scores of B-allel Frequencies
#DBSNP ID collecting system
for i in dbsnpf.readlines():
temp = i.split('\t')
ID = str(temp[0])+"_"+str(temp[2])
scores[ID] = 0
count = count + 1
feature_list[file] = []
#VCF file PROCESSING and Generation of features
for i in f.readlines():
if i.startswith("#"):
continue
temp = i.split('\t')
values = temp[7].split(';')
if values[0].startswith("INDEL"):
continue
for j in values:
if j.startswith("DP4"):
readcounts = j.split(',')
readcounts[0] = readcounts[0][4:]
score = (float(readcounts[2]) + float(readcounts[3])) / (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
depth = depth + (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
ID = str(temp[0]) + "_" + str(temp[1])
feature_list[file].append(ID)
scores[ID]= score
sum = sum + float(readcounts[2]) + float(readcounts[3])
mean_depth[file] = depth / count
sum_file[file] = sum
for key in features:
if glob_scores.has_key(file):
glob_scores[file].append(scores[key])
else:
glob_scores[file] = [scores[key]]
dbsnpf.close()
f.close()
for key in sorted(glob_scores):
label.append(key)
#create dataset from the VCF list files
def createDataSetFromList(base_list, bedFile):
base_F = open(base_list,'r')
for line in base_F.readlines():
link = line.strip()
f = open(link, "r")
dbsnpf= open(bedFile,"r")
file = link[link.rindex("/")+1:]
depth = 0
count = 0
sum = 0
scores = dict() # Scores of B-allel Frequencies
#DBSNP ID collecting system
for i in dbsnpf.readlines():
temp = i.split('\t')
ID = str(temp[0])+"_"+str(temp[2])
scores[ID] = 0
count = count + 1
feature_list[file] = []
#VCF file PROCESSING and Generation of features
for i in f.readlines():
if i.startswith("#"):
continue
temp = i.split('\t')
values = temp[7].split(';')
if values[0].startswith("INDEL"):
continue
for j in values:
if j.startswith("DP4"):
readcounts = j.split(',')
readcounts[0] = readcounts[0][4:]
score = (float(readcounts[2]) + float(readcounts[3])) / (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
depth = depth + (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
ID = str(temp[0]) + "_" + str(temp[1])
feature_list[file].append(ID)
scores[ID]= score
sum = sum + float(readcounts[2]) + float(readcounts[3])
mean_depth[file] = depth / count
sum_file[file] = sum
for key in features:
if glob_scores.has_key(file):
glob_scores[file].append(scores[key])
else:
glob_scores[file] = [scores[key]]
dbsnpf.close()
f.close()
for key in sorted(glob_scores):
label.append(key)
def createDataSetFromDir_TEST(base_dir, bedFile,order):
for root, dirs, files in os.walk(base_dir):
for file in files:
if not file.endswith(".vcf"):
continue
link = root + '/' + file
f = open(link, "r")
dbsnpf= open(bedFile,"r")
depth = 0
count = 0
sum = 0
scores = dict() # Scores of B-allel Frequencies
#DBSNP ID collecting system
for i in dbsnpf.readlines():
temp = i.split('\t')
ID = str(temp[0])+"_"+str(temp[2])
scores[ID] = 0
count = count + 1
file = file + "_" + order
feature_list[file] = []
#VCF file PROCESSING and Generation of features
for i in f.readlines():
if i.startswith("#"):
continue
temp = i.split('\t')
values = temp[7].split(';')
if values[0].startswith("INDEL"):
continue
for j in values:
if j.startswith("DP4"):
readcounts = j.split(',')
readcounts[0] = readcounts[0][4:]
score = (float(readcounts[2]) + float(readcounts[3])) / (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
depth = depth + (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
ID = str(temp[0]) + "_" + str(temp[1])
feature_list[file].append(ID)
scores[ID]= score
sum = sum + float(readcounts[2]) + float(readcounts[3])
mean_depth[file] = depth / count
sum_file[file] = sum
for key in features:
if glob_scores.has_key(file):
glob_scores[file].append(scores[key])
else:
glob_scores[file] = [scores[key]]
dbsnpf.close()
f.close()
for key in sorted(glob_scores):
label.append(key)
#create dataset from the VCF list files
def createDataSetFromList_TEST(base_list, bedFile,order):
base_F = open(base_list,'r')
for line in base_F.readlines():
link = line.strip()
f = open(link, "r")
dbsnpf= open(bedFile,"r")
file = link[link.rindex("/")+1:]
depth = 0
count = 0
sum = 0
scores = dict() # Scores of B-allel Frequencies
#DBSNP ID collecting system
for i in dbsnpf.readlines():
temp = i.split('\t')
ID = str(temp[0])+"_"+str(temp[2])
scores[ID] = 0
count = count + 1
file = file + "_" + order
feature_list[file] = []
#VCF file PROCESSING and Generation of features
for i in f.readlines():
if i.startswith("#"):
continue
temp = i.split('\t')
values = temp[7].split(';')
if values[0].startswith("INDEL"):
continue
for j in values:
if j.startswith("DP4"):
readcounts = j.split(',')
readcounts[0] = readcounts[0][4:]
score = (float(readcounts[2]) + float(readcounts[3])) / (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
depth = depth + (float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
ID = str(temp[0]) + "_" + str(temp[1])
feature_list[file].append(ID)
scores[ID]= score
sum = sum + float(readcounts[2]) + float(readcounts[3])
mean_depth[file] = depth / count
sum_file[file] = sum
for key in features:
if glob_scores.has_key(file):
glob_scores[file].append(scores[key])
else:
glob_scores[file] = [scores[key]]
dbsnpf.close()
f.close()
for key in sorted(glob_scores):
label.append(key)
# kNN based classification
def clustering(K):
altFreqList = []
keyList = []
Pos_count = 0
for key in sorted(glob_scores):
altFreqList.append(glob_scores[key])
keyList.append(key)
dataSetSize = len(altFreqList)
sum = 0
othersum = 0
for target in range(0,dataSetSize):
dist = []
pheno = []
# comparison to the other samples based on BASE sample
base = altFreqList[target]
tempA = set(feature_list[keyList[target]])
# calculate eucladian distance between two samples
for i in range(0, dataSetSize):
# IsdiffPhenotype = 0.0
comparison = altFreqList[i]
tempB = set(feature_list[keyList[i]])
selected_feature = tempA.intersection(tempB)
# IsdiffPhenotype = (2*len(selected_feature))/(len(tempA) + len(tempB))
vecA = []
vecB = []
idx = 0
for k in features:
if k in selected_feature:
vecA.append(base[idx])
vecB.append(comparison[idx])
idx = idx + 1
distance = pearson_def(vecA, vecB)
dist.append(distance)
# pheno.append(IsdiffPhenotype)
orderCount = 0
while (orderCount < K):
max_value = sorted(dist)[-2-orderCount]
max_indice = dist.index(max_value)
sum = sum + max_value
Pos_count = Pos_count + 1
outPOS=str(label[target]) + "\tmatched to\t" + str(label[max_indice])+ "\tscore=\t" + str(max_value)
print outPOS
#POS_F.write(outPOS + "\n")
orderCount = orderCount + 1
# print sum/Pos_count
#OLD version
def classify(T):
altFreqList = []
keyList = []
Pos_count = 0
Neg_count = 0
POS_F = open("/data/users/sjlee/valid_qc/WGS/SNP/results/TEST2_POS_SNP.txt",'w')
NEG_F = open("/data/users/sjlee/valid_qc/WGS/SNP/results/TEST2_NEG_SNP.txt",'w')
for key in sorted(glob_scores):
altFreqList.append(glob_scores[key])
keyList.append(key)
dataSetSize = len(altFreqList)
sum = 0
othersum = 0
for target in range(0,dataSetSize):
dist = []
pheno = []
# comparison to the other samples based on BASE sample
base = altFreqList[target]
tempA = set(feature_list[keyList[target]])
# calculate eucladian distance between two samples
for i in range(0, dataSetSize):
IsdiffPhenotype = 0.0
comparison = altFreqList[i]
tempB = set(feature_list[keyList[i]])
selected_feature = tempA.intersection(tempB)
IsdiffPhenotype = (2*len(selected_feature))/(len(tempA) + len(tempB))
vecA = []
vecB = []
idx = 0
for k in features:
if k in selected_feature:
vecA.append(base[idx])
vecB.append(comparison[idx])
idx = idx + 1
distance = pearson_def(vecA, vecB)
dist.append(distance)
pheno.append(IsdiffPhenotype)
for value in sorted(dist)[0:-2]:
if abs((Tmean-value)/Tstd) < abs((Fmean-value)/Fstd):
max_value = value
max_indice = dist.index(max_value)
td = array(dist)
sum = sum + max_value
Pos_count = Pos_count + 1
outPOS=str(label[target]) + "\tmatched to\t" + str(label[max_indice])+ "\tscore=\t" + str(max_value) + "\tdiff=\t" + str(pheno[max_indice])
POS_F.write(outPOS + "\n")
else:
max_value = value
max_indice = dist.index(max_value)
othersum = othersum + max_value
Neg_count = Neg_count + 1
outNEG=str(label[target]) + "\tmatched to\t" + str(label[max_indice])+ "\tscore=\t" + str(max_value) + "\tdiff=\t" + str(pheno[max_indice])
NEG_F.write(outNEG + "\n")
print sum/Pos_count
print othersum/Neg_count
POS_F.close()
NEG_F.close()
def classifyNV(vec2Classify, p0Vec, p0S, p1Vec, p1S):
if abs(p0Vec - vec2Classify) + p0S > abs(p1Vec - vec2Classify) - p1S:
return abs((abs(p0Vec - vec2Classify) + p0S )/ (abs(p1Vec - vec2Classify) - p1S )), 1
else:
return abs((abs(p0Vec - vec2Classify) + p0S) / (abs(p1Vec - vec2Classify) - p1S)), 0
# if depth < 5:
# if (vec2Classify >= (p1Vec - p1S)):
# return (abs(p0Vec - vec2Classify) / p0S )/ (abs(p1Vec - vec2Classify)/ p1S ), 1
# else:
# return (abs(p0Vec - vec2Classify) / p0S) / (abs(p1Vec - vec2Classify)/ p1S), 0
# else:
# if (abs(p0Vec - vec2Classify) / p0S > abs(p1Vec - vec2Classify)/ p1S):
# return (abs(p0Vec - vec2Classify) / p0S )/ (abs(p1Vec - vec2Classify)/ p1S ), 1
# else:
# return (abs(p0Vec - vec2Classify) / p0S) / (abs(p1Vec - vec2Classify)/ p1S), 0
def trainNV(trainMatrix,trainCategory):
numTrainDocs = len(trainMatrix) # #of traning samples
p1List = []
p0List = []
for i in range(numTrainDocs):
if trainCategory[i] == 1:
p1List.append(trainMatrix[i])
else:
p0List.append(trainMatrix[i])
return mean(p1List),std(p1List), mean(p0List),std(p0List)
def calAUC(predStrengths, classLabels):
ySum = 0.0 #variable to calculate AUC
cur = (1.0,1.0) #cursor
numPosClas = sum(array(classLabels)==1.0)
yStep = 1/float(numPosClas); xStep = 1/float(len(classLabels)-numPosClas)
sortedIndicies = predStrengths.argsort()#get sorted index, it's reverse
#loop through all the values, drawing a line segment at each point
for index in sortedIndicies.tolist()[0]:
if classLabels[index] == 1:
delX = 0; delY = yStep;
else:
delX = xStep; delY = 0;
ySum += cur[1]
cur = (cur[0]-delX,cur[1]-delY)
return ySum*xStep
#def plotROC(predStrengths, classLabels):
# import matplotlib.pyplot as plt
# cur = (1.0,1.0) #cursor
# ySum = 0.0 #variable to calculate AUC
# numPosClas = sum(array(classLabels)==1.0)
# yStep = 1/float(numPosClas); xStep = 1/float(len(classLabels)-numPosClas)
# sortedIndicies = predStrengths.argsort()#get sorted index, it's reverse
# fig = plt.figure()
# fig.clf()
# ax = plt.subplot(111)
# #loop through all the values, drawing a line segment at each point
# for index in sortedIndicies.tolist()[0]:
# if classLabels[index] == 1:
# delX = 0; delY = yStep;
# else:
# delX = xStep; delY = 0;
# ySum += cur[1]
# #draw line from cur to (cur[0]-delX,cur[1]-delY)
# ax.plot([cur[0],cur[0]-delX],[cur[1],cur[1]-delY], c='b')
# cur = (cur[0]-delX,cur[1]-delY)
# ax.plot([0,1],[0,1],'b--')
# plt.xlabel('False positive rate'); plt.ylabel('True positive rate')
# plt.title('ROC curves')
# ax.axis([0,1,0,1])
# plt.show()
# print "the Area Under the Curve is: ",ySum*xStep
def getPredefinedModel(depth):
if Family_flag:
if depth > 10:
return 0.874611,0.022596,0.644481,0.020908
elif depth > 5:
return 0.785312,0.021318,0.596133,0.022502
elif depth > 2:
return 0.650299,0.019252,0.5346,0.020694
elif depth > 1:
return 0.578582,0.018379,0.495017,0.021652
elif depth > 0.5:
return 0.524757,0.023218,0.465653,0.027378
else:
# print "Warning: Sample region depth is too low < 1"
return 0.524757,0.023218, 0.465653, 0.027378
else:
if depth > 10:
return 0.874546, 0.022211, 0.310549, 0.060058
elif depth > 5:
return 0.785249,0.021017, 0.279778, 0.054104
elif depth > 2:
return 0.650573, 0.018699,0.238972, 0.047196
elif depth > 1:
return 0.578386,0.018526, 0.222322, 0.041186
elif depth > 0.5:
return 0.529327,0.025785, 0.217839, 0.040334
else:
# print "Warning: Sample region depth is too low < 1"
return 0.529327,0.025785, 0.217839, 0.040334
# if depth > 30:
# return 0.874546, 0.022211, 0.310549, 0.060058
# elif depth > 10:
# return 0.785249,0.021017, 0.279778, 0.054104
# elif depth > 5:
# return 0.650573, 0.018699,0.238972, 0.047196
# elif depth > 2:
# return 0.578386,0.018526, 0.222322, 0.041186
# elif depth > 1:
# return 0.529327,0.025785, 0.217839, 0.040334
# else:
# print "Warning: Sample region depth is too low < 1"
# return 0.529327,0.025785, 0.217839, 0.040334
# if depth > 0.1:
# return 0.0351* depth + 0.5538, 0.02, 0.009977*depth + 0.216978, 0.045
# else:
# print "too low depth"
# return 0.529327,0.025785, 0.217839, 0.040334
# if depth > 0.5:
# return 0.06315* (math.log(depth)) + 0.64903, 0.046154, 0.0005007*depth + 0.3311504,0.12216
# else:
# return 0.62036, 0.046154, 0.31785, 0.12216
def getPredefinedModel_F(depth):
if depth > 10:
return 0.874546, 0.022211, 0.620549, 0.060058
elif depth > 5:
return 0.785249,0.021017, 0.609778, 0.054104
elif depth > 2:
return 0.650573, 0.018699,0.548972, 0.047196
elif depth > 1:
return 0.578386,0.018526, 0.502322, 0.041186
elif depth > 0.5:
return 0.529327,0.025785, 0.457839, 0.040334
else:
# print "Warning: Sample region depth is too low < 1"
return 0.529327,0.025785, 0.457839, 0.040334
# if depth > 30:
# return 0.874546, 0.022211, 0.310549, 0.060058
# elif depth > 10:
# return 0.785249,0.021017, 0.279778, 0.054104
# elif depth > 5:
# return 0.650573, 0.018699,0.238972, 0.047196
# elif depth > 2:
# return 0.578386,0.018526, 0.222322, 0.041186
# elif depth > 1:
# return 0.529327,0.025785, 0.217839, 0.040334
# else:
# print "Warning: Sample region depth is too low < 1"
# return 0.529327,0.025785, 0.217839, 0.040334
# if depth > 0.1:
# return 0.0351* depth + 0.5538, 0.02, 0.009977*depth + 0.216978, 0.045
# else:
# print "too low depth"
# return 0.529327,0.025785, 0.217839, 0.040334
# if depth > 0.5:
# return 0.06315* (math.log(depth)) + 0.64903, 0.046154, 0.0005007*depth + 0.3311504,0.12216
# else:
# return 0.62036, 0.046154, 0.31785, 0.12216
def classifying():
AUCs =[]
wholeFeatures = 50
temp =[]
altFreqList = []
keyList = []
for key in sorted(glob_scores):
altFreqList.append(glob_scores[key])
keyList.append(key)
dataSetSize = len(altFreqList)
filter_list = []
for i in range(0, dataSetSize):
for j in range(0, dataSetSize):
if i!=j:
if keyList[j] not in filter_list:
temp.append([keyList[i],keyList[j]])
filter_list.append(keyList[i])
for iterations in range(49,wholeFeatures):
samples = []
numFeatures = iterations
count = 0
for i in range(0,len(temp)):
tempA = set(feature_list[temp[i][0].strip()])
tempB = set(feature_list[temp[i][1].strip()])
selected_feature = tempA.intersection(tempB)
vecA = []
vecB = []
idx = 0
for k in features:
if k in selected_feature:
vecA.append(glob_scores[temp[i][0].strip()][idx])
vecB.append(glob_scores[temp[i][1].strip()][idx])
idx = idx + 1
distance = pearson_def(vecA, vecB)
samples.append(distance)
predStrength = []
training_flag =0
####0715 Append
output_matrix_f = open(outdir + "/output_corr_matrix.txt","w")
output_matrix = dict()
if out_tag!="stdout":
out_f = open(outdir + "/" + out_tag + "_all.txt","w")
for i in range(0, len(samples)):
output_matrix[temp[i][0]] = dict()
for j in range(0,len(samples)):
output_matrix[temp[i][0]][temp[j][0]] = 0
if training_flag == 1:
#make training set
for i in range(0,len(samples)):
trainMatrix= []
trainCategory = []
for j in range(0, len(samples)):
if i==j:
continue
else:
trainMatrix.append(samples[j])
trainCategory.append(classLabel[j])
#training samples in temp
#p0V, p1V, pAb = trainNB0(array(trainMatrix),array(trainCategory))
p1V,p1S, p0V, p0S = trainNV(array(trainMatrix),array(trainCategory))
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] == 1:
print str(temp[i][0]) + '\tsample is matched to\t',str(temp[i][1]),'\t', samples[i]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
else :
for i in range(0,len(samples)):
depth = min(mean_depth[temp[i][0].strip()],mean_depth[temp[i][1].strip()])
p1V,p1S, p0V, p0S = getPredefinedModel(depth)
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] ==1:
output_matrix[temp[i][0].strip()][temp[i][1].strip()] = samples[i]
if out_tag=="stdout":
print str(temp[i][0][:-4]) + '\tmatched\t',str(temp[i][1][:-4]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-4]) + '\tmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
else:
if out_tag=="stdout":
print str(temp[i][0][:-4]) + '\tunmatched\t',str(temp[i][1][:-4]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-4]) + '\tunmatched\t' + str(temp[i][1][:-4]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
#print sum_file[temp[i][0]],sum_file[temp[i][1].strip()]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
#testing sample is samples
output_matrix_f.write("sample_ID")
for key in output_matrix.keys():
output_matrix_f.write("\t" + key[0:key.index('.')])
output_matrix_f.write("\n")
for key in output_matrix.keys():
output_matrix_f.write(key[0:key.index('.')])
for otherkey in output_matrix.keys():
output_matrix_f.write("\t" + str(output_matrix[key][otherkey]))
output_matrix_f.write("\n")
output_matrix_f.close()
if out_tag!="stdout":
out_f.close()
def classifying_test():
AUCs =[]
wholeFeatures = 50
temp = []
keyF = open(testsamplename,'r')
temp =[]
for k in keyF.readlines():
keyfile = k.split(":")
keyfile[0] = keyfile[0].strip() + "_1"
keyfile[1] = keyfile[1].strip() + "_2"
temp.append(keyfile)
keyF.close()
for iterations in range(49,wholeFeatures):
samples = []
numFeatures = iterations
count = 0
for i in range(0,len(temp)):
tempA = set(feature_list[temp[i][0].strip()])
tempB = set(feature_list[temp[i][1].strip()])
selected_feature = tempA.intersection(tempB)
vecA = []
vecB = []
idx = 0
for k in features:
if k in selected_feature:
vecA.append(glob_scores[temp[i][0].strip()][idx])
vecB.append(glob_scores[temp[i][1].strip()][idx])
idx = idx + 1
distance = pearson_def(vecA, vecB)
samples.append(distance)
predStrength = []
training_flag =0
####0715 Append
output_matrix_f = open(outdir + "/output_corr_matrix.txt","w")
output_matrix = dict()
if out_tag!="stdout":
out_f = open(outdir + "/" + out_tag + ".txt","w")
for i in range(0, len(samples)):
output_matrix[temp[i][0]] = dict()
for j in range(0,len(samples)):
output_matrix[temp[i][0]][temp[j][0]] = 0
if training_flag == 1:
#make training set
for i in range(0,len(samples)):
trainMatrix= []
trainCategory = []
for j in range(0, len(samples)):
if i==j:
continue
else:
trainMatrix.append(samples[j])
trainCategory.append(classLabel[j])
#training samples in temp
#p0V, p1V, pAb = trainNB0(array(trainMatrix),array(trainCategory))
p1V,p1S, p0V, p0S = trainNV(array(trainMatrix),array(trainCategory))
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] == 1:
print str(temp[i][0]) + '\tsample is matched to\t',str(temp[i][1]),'\t', samples[i]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
else :
for i in range(0,len(samples)):
depth = min(mean_depth[temp[i][0].strip()],mean_depth[temp[i][1].strip()])
p1V,p1S, p0V, p0S = getPredefinedModel(depth)
result = classifyNV(samples[i],p0V,p0S, p1V, p1S)
if result[1] ==1:
output_matrix[temp[i][0].strip()][temp[i][1].strip()] = samples[i]
if out_tag=="stdout":
print str(temp[i][0][:-6]) + '\tmatched\t',str(temp[i][1][:-6]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0][:-6]) + '\tmatched\t' + str(temp[i][1][:-6]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
#print sum_file[temp[i][0]],sum_file[temp[i][1].strip()]
predStrength.append(result[0])
# AUCs.append(calAUC(mat(predStrength),classLabel))
# plotROC(mat(predStrength),classLabel)
# print AUCs
#testing sample is samples
output_matrix_f.write("sample_ID")
for key in output_matrix.keys():
output_matrix_f.write("\t" + key[0:key.index('.')])
output_matrix_f.write("\n")
for key in output_matrix.keys():
output_matrix_f.write(key[0:key.index('.')])
for otherkey in output_matrix.keys():
output_matrix_f.write("\t" + str(output_matrix[key][otherkey]))
output_matrix_f.write("\n")
output_matrix_f.close()
if out_tag!="stdout":
out_f.close()
def generate_R_scripts():
r_file = open(outdir + "/r_script.r","w")
cmd = "output_corr_matrix <- read.delim(\"" + outdir + "/output_corr_matrix.txt\")\n"
cmd = cmd + "data = output_corr_matrix\n"
cmd = cmd + "d3 <- as.dist((1 - data[,-1]))\n"
cmd = cmd + "clust3 <- hclust(d3, method = \"average\")\n"
cmd = cmd + "pdf(\"" +outdir+ "/" + pdf_tag + ".pdf\", width=10, height=7)\n"
cmd = cmd + "op = par(bg = \"gray85\")\n"
cmd = cmd + "par(plt=c(0.05, 0.95, 0.5, 0.9))\n"
cmd = cmd + "plot(clust3, lwd = 2, lty = 1,cex=0.8, xlab=\"Samples\", sub = \"\", ylab=\"Distance (1-Pearson correlation)\",hang = -1, axes = FALSE)\n"
cmd = cmd + "axis(side = 2, at = seq(0, 1, 0.2), labels = FALSE, lwd = 2)\n"
cmd = cmd + "mtext(seq(0, 1, 0.2), side = 2, at = seq(0, 1, 0.2), line = 1, las = 2)\n"
cmd = cmd + "dev.off()\n"
r_file.write(cmd)
r_file.close()
def run_R_scripts():
command = "R CMD BATCH " + outdir + "/r_script.r"
proc = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
return_code = proc.wait()
def remove_internal_files():
if outdir.find("*"):
sys.exit()
command = "rm -rf " + outdir + "/output_corr_matrix.txt"
proc = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
return_code = proc.wait()
command = "rm -rf " + outdir + "/r_script.r"
proc = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
return_code = proc.wait()
command = "rm -rf " + outdir + "/r_script.r.Rout"
proc = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
return_code = proc.wait()
def run_mpileup():
SAMTOOLS=""
BCFTOOLS=""
REF=""
with open("./ncm.conf",'r') as F:
for line in F.readlines():
temp = line.split('\"')
if temp[0].startswith("SAMTOOLS"):
SAMTOOLS = temp[1].strip()
elif temp[0].startswith("BCFTOOLS"):
BCFTOOLS = temp[1].strip()
elif temp[0].startswith("REF"):
REF = temp[1].strip()
# REF="/NAS/nas33-2/mpileup/hg19.fasta"
for sample in bam_list:
filename = sample.split("/")
tag = filename[-1][0:filename[-1].rindex(".")]
command = SAMTOOLS + " mpileup -I -uf " + REF + " -l " + bedFile + " " + sample + " | " + BCFTOOLS + " view -cg - > " + outdir + "/" + tag + ".vcf"
print command
call(command,shell=True)
# proc = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)
# return_code = proc.wait()
def find_bam_list():
for root, dirs, files in os.walk(base_dir):
for file in files:
if not file.endswith(".bam"):
continue
bam_list.append(root + "/" + file)
def get_bam_list():
with open(base_list,'r') as F:
for line in F.readlines():
bam_list.append(line.strip())
def output_filter():
success_set_M = []
success_set_U = []
failure_set_M = []
failure_set_U = []
with open(outdir + "/" + out_tag + "_all.txt","r") as F:
for line in F.readlines():
temp = line.strip().split('\t')
sample1 = temp[0]
sample2 = temp[2]
match = temp[1]
if match == "matched":
if sample1[sample1.index("TCGA"):sample1.index("TCGA")+12] == sample2[sample2.index("TCGA"):sample2.index("TCGA")+12] :
success_set_M.append(line)
else:
failure_set_M.append(line)
elif match == "unmatched":
if sample1[sample1.index("TCGA"):sample1.index("TCGA")+12] == sample2[sample2.index("TCGA"):sample2.index("TCGA")+12] :
failure_set_U.append(line)
else:
success_set_U.append(line)
Matched_file = open(outdir + "/" + out_tag + "_matched.txt",'w')
for i in success_set_M:
Matched_file.write(i)
for i in failure_set_M:
Matched_file.write(i)
Matched_file.close()
problem_file = open(outdir + "/" + out_tag + "_problematic.txt",'w')
for i in failure_set_M:
problem_file.write(i)
for i in failure_set_U:
problem_file.write(i)
problem_file.close()
Summary_file = open(outdir + "/" + out_tag + "_summary.txt",'w')
## paired cluster - only failed things
Summary_file.write("###########################################\n")
Summary_file.write("### Problematic clusters of same orgins ##\n")
Summary_file.write("###########################################\n\n")
cluster = dict()
result_set = failure_set_M + success_set_M
for line in result_set:
temp = line.strip().split('\t')
flag = 0
for key in cluster:
if temp[0] in cluster[key]:
cluster[key].add(temp[2])
flag = 1
break
elif temp[2] in cluster[key]:
cluster[key].add(temp[0])
flag = 1
break
if flag == 0:
cluster[temp[0]] = set()
cluster[temp[0]].add(temp[0])