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ncm.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()
real_depth = dict()
real_count = dict()
sum_file = dict()
out_tag = ""
pdf_tag = ""
Family_flag = False
Nonzero_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 = dict()
depth[file] = 0
real_count[file] = 0
count = 0
sum=dict()
sum[file] = 0
scores = dict() # Scores of B-allel Frequencies
#DBSNP ID collecting system
for i in dbsnpf.readlines():
temp = i.strip().split('\t')
if temp[0].find("chr")!= -1:
ID = str(temp[0][3:]) + "_" + str(temp[2])
else:
ID = str(temp[0]) + "_" + str(temp[2])
scores[ID] = 0
count = count + 1
## 0618_samtools and haplotyper
vcf_flag = 0
# feature_list[file] = []
score_set = dict()
#VCF file PROCESSING and Generation of features
total = 0
GVCF_samples = dict()
for i in f.readlines():
if i.startswith("#"):
if i.find("DP4") != -1:
vcf_flag = 1
if i.find("#CHROM") != -1:
temp = i.strip().split('\t')
total=len(temp) - 9
if total != 1:
for sample_idx in range(0,total):
file = temp[sample_idx + 9]
GVCF_samples[temp[sample_idx + 9]] = []
score_set[temp[sample_idx + 9]] = dict()
depth[temp[sample_idx + 9]] = 0
real_count[temp[sample_idx + 9]] = 0
sum[temp[sample_idx + 9]] =0
feature_list[temp[sample_idx + 9]] = []
if total == 1:
feature_list[file] = []
continue
temp = i.strip().split('\t')
## ID in BED file only
if temp[0].find("chr")!= -1:
ID = str(temp[0][3:]) + "_" + str(temp[1])
else:
ID = str(temp[0]) + "_" + str(temp[1])
if ID not in scores:
continue
if vcf_flag == 1:
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:]
total_reads =(float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
score = 0
if total_reads > 0:
score = (float(readcounts[2]) + float(readcounts[3])) / total_reads
real_count[file] = real_count[file] + 1
depth[file] = depth[file] + total_reads
if ID in scores:
feature_list[file].append(ID)
scores[ID]= score
sum[file] = sum[file] + float(readcounts[2]) + float(readcounts[3])
elif total == 1 and vcf_flag == 0:
format = temp[8].split(':') ##Format
AD_idx = -1
DP_idx = -1
for idx in range(0,len(format)):
if format[idx] == "AD":
AD_idx = idx
elif format[idx] == "DP":
DP_idx = idx
if AD_idx == -1:
continue
if DP_idx == -1:
continue
idx = 9
values = temp[idx].split(":")
readcounts = values[AD_idx].split(',')
if float(readcounts[0]) + float(readcounts[1]) < 0.5:
score =0
else:
score = float(readcounts[1])/ (float(readcounts[0]) + float(readcounts[1]))
depth[file] = depth[file] + float(values[DP_idx])
if float(values[DP_idx]) > 0:
real_count[file] = real_count[file] + 1
if ID in scores:
feature_list[file].append(ID)
scores[ID]= score ##from here!
sum[file] = sum[file] + float(readcounts[1])
else: ###### Haplotyper or other VCF
format = temp[8].split(':') ##Format
AD_idx = -1
DP_idx = -1
for idx in range(0,len(format)):
if format[idx] == "AD":
AD_idx = idx
elif format[idx] == "DP":
DP_idx = idx
if AD_idx == -1:
continue
if DP_idx == -1:
continue
idx = 9
for file in GVCF_samples:
values = temp[idx].split(":")
if len(values) < len(format):
score = 0
idx = idx + 1
continue
readcounts = values[AD_idx].split(',')
if float(readcounts[0]) + float(readcounts[1]) < 0.5:
score =0
else:
score = float(readcounts[1])/ (float(readcounts[0]) + float(readcounts[1]))
depth[file] = depth[file] + float(values[DP_idx])
if float(values[DP_idx]) > 0:
real_count[file] = real_count[file] + 1
if ID in scores:
feature_list[file].append(ID)
score_set[file][ID]= score ##from here!
sum[file] = sum[file] + float(readcounts[1])
idx = idx + 1
## TOTAL is not 1 or total is 1 cases
if total == 1:
mean_depth[file] = depth[file] / float(count)
real_depth[file] = depth[file] / float(real_count[file])
sum_file[file] = sum[file]
for key in features:
if glob_scores.has_key(file):
glob_scores[file].append(scores[key])
else:
glob_scores[file] = [scores[key]]
else:
for file in GVCF_samples:
mean_depth[file] = depth[file] / float(count)
real_depth[file] = depth[file] / float(real_count[file])
sum_file[file] = sum[file]
for key in features:
if key not in score_set[file]:
score_set[file][key] = 0
if glob_scores.has_key(file):
glob_scores[file].append(score_set[file][key])
else:
glob_scores[file] = [score_set[file][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 = dict()
depth[file] = 0
real_count[file] = 0
count = 0
sum=dict()
sum[file] = 0
scores = dict() # Scores of B-allel Frequencies
#DBSNP ID collecting system
for i in dbsnpf.readlines():
temp = i.strip().split('\t')
if temp[0].find("chr")!= -1:
ID = str(temp[0][3:]) + "_" + str(temp[2])
else:
ID = str(temp[0]) + "_" + str(temp[2])
scores[ID] = 0
count = count + 1
## 0618_samtools and haplotyper
vcf_flag = 0
# feature_list[file] = []
score_set = dict()
#VCF file PROCESSING and Generation of features
total = 0
GVCF_samples = dict()
for i in f.readlines():
if i.startswith("#"):
if i.find("DP4") != -1:
vcf_flag = 1
if i.find("#CHROM") != -1:
temp = i.strip().split('\t')
total=len(temp) - 9
if total != 1:
for sample_idx in range(0,total):
file = temp[sample_idx + 9]
GVCF_samples[temp[sample_idx + 9]] = []
score_set[temp[sample_idx + 9]] = dict()
depth[temp[sample_idx + 9]] = 0
real_count[temp[sample_idx + 9]] = 0
sum[temp[sample_idx + 9]] =0
feature_list[temp[sample_idx + 9]] = []
if total == 1:
feature_list[file] = []
continue
temp = i.strip().split('\t')
## ID in BED file only
if temp[0].find("chr")!= -1:
ID = str(temp[0][3:]) + "_" + str(temp[1])
else:
ID = str(temp[0]) + "_" + str(temp[1])
if ID not in scores:
continue
if vcf_flag == 1:
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:]
total_reads =(float(readcounts[0]) + float(readcounts[1]) + float(readcounts[2]) + float(readcounts[3]))
score = 0
if total_reads > 0:
score = (float(readcounts[2]) + float(readcounts[3])) / total_reads
real_count[file] = real_count[file] + 1
depth[file] = depth[file] + total_reads
if ID in scores:
feature_list[file].append(ID)
scores[ID]= score
sum[file] = sum[file] + float(readcounts[2]) + float(readcounts[3])
elif total == 1 and vcf_flag == 0:
format = temp[8].split(':') ##Format
AD_idx = -1
DP_idx = -1
for idx in range(0,len(format)):
if format[idx] == "AD":
AD_idx = idx
elif format[idx] == "DP":
DP_idx = idx
if AD_idx == -1:
continue
if DP_idx == -1:
continue
idx = 9
values = temp[idx].split(":")
readcounts = values[AD_idx].split(',')
if float(readcounts[0]) + float(readcounts[1]) < 0.5:
score =0
else:
score = float(readcounts[1])/ (float(readcounts[0]) + float(readcounts[1]))
depth[file] = depth[file] + float(values[DP_idx])
if float(values[DP_idx]) > 0:
real_count[file] = real_count[file] + 1
if ID in scores:
feature_list[file].append(ID)
scores[ID]= score ##from here!
sum[file] = sum[file] + float(readcounts[1])
else: ###### Haplotyper or other VCF
format = temp[8].split(':') ##Format
AD_idx = -1
DP_idx = -1
for idx in range(0,len(format)):
if format[idx] == "AD":
AD_idx = idx
elif format[idx] == "DP":
DP_idx = idx
if AD_idx == -1:
continue
if DP_idx == -1:
continue
idx = 9
for file in GVCF_samples:
values = temp[idx].split(":")
if len(values) < len(format):
score = 0
idx = idx + 1
continue
readcounts = values[AD_idx].split(',')
if float(readcounts[0]) + float(readcounts[1]) < 0.5:
score =0
else:
score = float(readcounts[1])/ (float(readcounts[0]) + float(readcounts[1]))
depth[file] = depth[file] + float(values[DP_idx])
if float(values[DP_idx]) > 0:
real_count[file] = real_count[file] + 1
if ID in scores:
feature_list[file].append(ID)
score_set[file][ID]= score ##from here!
sum[file] = sum[file] + float(readcounts[1])
idx = idx + 1
## TOTAL is not 1 or total is 1 cases
if total == 1:
mean_depth[file] = depth[file] / float(count)
real_depth[file] = depth[file] / float(real_count[file])
sum_file[file] = sum[file]
for key in features:
if glob_scores.has_key(file):
glob_scores[file].append(scores[key])
else:
glob_scores[file] = [scores[key]]
else:
for file in GVCF_samples:
mean_depth[file] = depth[file] / float(count)
real_depth[file] = depth[file] / float(real_count[file])
sum_file[file] = sum[file]
for key in features:
if key not in score_set[file]:
score_set[file][key] = 0
if glob_scores.has_key(file):
glob_scores[file].append(score_set[file][key])
else:
glob_scores[file] = [score_set[file][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.strip().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.strip().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")
out_matched = open(outdir + "/" + out_tag + "_matched.txt","w")
for i in range(0, len(keyList)):
output_matrix[keyList[i]] = dict()
for j in range(0,len(keyList)):
output_matrix[keyList[i]][keyList[j]] = 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])
else :
for i in range(0,len(samples)):
depth = 0
if Nonzero_flag:
depth = min(real_depth[temp[i][0].strip()],real_depth[temp[i][1].strip()])
else:
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]
output_matrix[temp[i][1].strip()][temp[i][0].strip()] = samples[i]
if out_tag=="stdout":
print str(temp[i][0]) + '\tmatched\t',str(temp[i][1]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0]) + '\tmatched\t' + str(temp[i][1]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
out_matched.write(str(temp[i][0]) + '\tmatched\t' + str(temp[i][1]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
else:
if out_tag=="stdout":
print str(temp[i][0]) + '\tunmatched\t',str(temp[i][1]),'\t', round(samples[i],4),'\t',round(depth,2)
else :
out_f.write(str(temp[i][0]) + '\tunmatched\t' + str(temp[i][1]) + '\t'+ str(round(samples[i],4)) + '\t' + str(round(depth,2)) + '\n')
predStrength.append(result[0])
#testing sample is samples
output_matrix_f.write("sample_ID")
for key in output_matrix.keys():
if key.find(".vcf") != -1:
output_matrix_f.write("\t" + key[0:key.index('.vcf')])
else:
output_matrix_f.write("\t" + key)
output_matrix_f.write("\n")
# for key in output_matrix.keys():
# for otherkey in output_matrix[key].keys():
# if output_matrix[key][otherkey] != 0:
# output_matrix[otherkey][key] = output_matrix[key][otherkey]
for key in output_matrix.keys():
if key.find(".vcf") != -1:
output_matrix_f.write(key[0:key.index('.vcf')])
else:
output_matrix_f.write(key)
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()
out_matched.close()
def classifying_test():
AUCs =[]
wholeFeatures = 50