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make_common_set.py
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make_common_set.py
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from __future__ import division
from __future__ import print_function
#import math
import numpy as np
import csv
#import scipy.misc
import pickle
#import pandas as pd
#from shapely.geometry import Polygon
#import gc
RT_min=10.0
path='/data/fzohora/dilution_series_syn_pep/feature_list/' #'/media/anne/Study/bsi/dilution_series_syn_peptide/feature_list/' #'/data/fzohora/water_raw_ms1/'
dataname=['130124_dilA_1_01','130124_dilA_1_02','130124_dilA_1_03','130124_dilA_1_04',
'130124_dilA_2_01','130124_dilA_2_02','130124_dilA_2_03','130124_dilA_2_04','130124_dilA_2_05','130124_dilA_2_06','130124_dilA_2_07',
'130124_dilA_8_01','130124_dilA_8_02','130124_dilA_8_03','130124_dilA_8_04',
'130124_dilA_9_01','130124_dilA_9_02','130124_dilA_9_03','130124_dilA_9_04','130124_dilA_10_01','130124_dilA_10_02', '130124_dilA_10_03', '130124_dilA_10_04', '130124_dilA_11_01', '130124_dilA_11_02', '130124_dilA_11_03', '130124_dilA_11_04', '130124_dilA_12_01', '130124_dilA_12_02', '130124_dilA_12_03', '130124_dilA_12_04']
feature_file=['130124_dilA_1_01.raw.fea.isotopes.csv','130124_dilA_1_02.raw.fea.isotopes.csv','130124_dilA_1_03.raw.fea.isotopes.csv','130124_dilA_1_04.raw.fea.isotopes.csv','130124_dilA_2_01.raw.fea.isotopes.csv','130124_dilA_2_02.raw.fea.isotopes.csv','130124_dilA_2_03.raw.fea.isotopes.csv','130124_dilA_2_04.raw.fea.isotopes.csv','130124_dilA_2_05.raw.fea.isotopes.csv','130124_dilA_2_06.raw.fea.isotopes.csv','130124_dilA_2_07.raw.fea.isotopes.csv','130124_dilA_8_01.raw.fea.isotopes.csv','130124_dilA_8_02.raw.fea.isotopes.csv','130124_dilA_8_03.raw.fea.isotopes.csv','130124_dilA_8_04.raw.fea.isotopes.csv','130124_dilA_9_01.raw.fea.isotopes.csv','130124_dilA_9_02.raw.fea.isotopes.csv','130124_dilA_9_03.raw.fea.isotopes.csv','130124_dilA_9_04.raw.fea.isotopes.csv','130124_dilA_10_01.raw.fea.isotopes.csv','130124_dilA_10_02.raw.fea.isotopes.csv', '130124_dilA_10_03.raw.fea.isotopes.csv', '130124_dilA_10_04.raw.fea.isotopes.csv', '130124_dilA_11_01.raw.fea.isotopes.csv', '130124_dilA_11_02.raw.fea.isotopes.csv', '130124_dilA_11_03.raw.fea.isotopes.csv', '130124_dilA_11_04.raw.fea.isotopes.csv', '130124_dilA_12_01.raw.fea.isotopes.csv', '130124_dilA_12_02.raw.fea.isotopes.csv', '130124_dilA_12_03.raw.fea.isotopes.csv', '130124_dilA_12_04.raw.fea.isotopes.csv']#, 'Demo_LC_Chymotrypsin.raw.fea.isotopes.csv', 'Demo_LC_Trypsin.raw.fea.isotopes.csv', 'Demo_HC_AspN.raw.fea.isotopes.csv', 'Demo_HC_Chymotrypsin.raw.fea.isotopes.csv', 'Demo_HC_Trypsin.raw.fea.isotopes.csv']
feature_count=[26515, 29696, 30785, 31985, 27345,26585,27750,28193,28474,28335,27475,25294, 22608, 22927, 23756, 22960, 23204, 23859, 23766, 26680, 19483, 24859, 25220, 28409, 25967, 30328, 29802, 32097, 30707, 32444, 33155]
delim=','
for data_index in range (15, len(dataname)): # 19, 20, 21
print(dataname[data_index])
#
#
# #-----------------------------------read peptide features summury-------------------------------------#
#
#
# #-----------------------------------read peptide features -------------------------------------#
filename_feature= feature_file[data_index]
total_feature=feature_count[data_index]
peptide_feature=np.zeros((total_feature, 16)) #0=mz, 1=rtstsr, 2=rtend, 3=z, 4=auc, 5=kept or removed or non_overlapping_feature_id, 6=end_mz, 7=min_rt, 8=max_rt, 9=endof2ndisotope, 10=start of second isotope, 11=overlapped/not, 12=maxI, 13=meanRT, 14=num of iso, 15=id of maxquant
isotope_gap=np.zeros((10))
isotope_gap[0]=0.01
isotope_gap[1]=1.00
isotope_gap[2]=0.500
isotope_gap[3]=0.333
isotope_gap[4]=0.250
isotope_gap[5]=0.200
isotope_gap[6]=0.167
isotope_gap[7]=0.143
isotope_gap[8]=0.125
isotope_gap[9]=0.111
mz_resolution=3
avoid=[]
f = open(path+'PEAKs/'+filename_feature, 'r')
line=f.readline()
line=f.readline()
i=0;
while line!='':
temp=line.split(',')
id=temp[0] # mz, rtstsr, rtend, z, auc
peptide_feature[i, 0]=round(float(temp[2]), mz_resolution) #mz
peptide_feature[i, 1]=round(float(temp[8]), 2) #st
peptide_feature[i, 2]=round(float(temp[9]), 2) #en
peptide_feature[i, 3]=temp[5] #charge
peptide_feature[i, 4]=temp[6] #area
peptide_feature[i, 6]=peptide_feature[i, 0] #??
peptide_feature[i, 12]=round(float(temp[10]), 2) #PeakI
peptide_feature[i, 13]=round(float(temp[3]), 2) #meanRT
line=f.readline()
min_rt=peptide_feature[i, 1]
max_rt=peptide_feature[i, 2]
isotope_no=0
while line!='':
temp=line.split(',')
if temp[0]!=id:
break
#else this isotope belongs to this same peptide
isotope_no=isotope_no+1
if isotope_no==1:
peptide_feature[i, 9]=round(float(temp[9]), 2) #end_of_second_isotope
peptide_feature[i, 10]=round(float(temp[8]), 2) #start_of_second_isotope
peptide_feature[i, 6]=round(peptide_feature[i, 6]+isotope_gap[int(peptide_feature[i, 3])], mz_resolution) #end_mz
if round(float(temp[8]), 2)<min_rt:
min_rt=round(float(temp[8]), 2) #st
if round(float(temp[9]), 2)>max_rt:
max_rt=round(float(temp[9]), 2) #en
line=f.readline()
peptide_feature[i, 7]=min_rt
peptide_feature[i, 8]=max_rt
peptide_feature[i, 14]=isotope_no+1
if peptide_feature[i, 7]<RT_min: #min_rt
avoid.append(i)
peptide_feature[i, 5]=-1
i=i+1
f.close()
##################################################################
# peptide_feature: feature list generated by PEAKs
# feature_list: feature_list generated by maxQuant
# feature_list=np.loadtxt(path+'maxQ/'+dataname[data_index]+'_2.csv', delimiter=delim)
filename ='/data/fzohora/dilution_series_syn_pep/feature_list/maxQ/'+dataname[data_index]+'_2.csv'
# initializing the titles and peptide_mascot list
MQ_peptide= []
# reading csv file
csvfile=open(filename, 'r')
# creating a csv reader object
csvreader = csv.reader(csvfile)
# extracting each data row one by one
for row in csvreader:
MQ_peptide.append(row)
csvfile.close()
feature_list=np.zeros((len(MQ_peptide), len(MQ_peptide[0]) ))
for i in range (0, len(MQ_peptide)):
for j in range (0, len(MQ_peptide[0])):
try:
feature_list[i, j]=MQ_peptide[i][j]
except:
feature_list[i, j]=0
#both list are sorted on m/z in asc order
mz_tolerance=0.01
RT_tolerance=0.03
mz_resolution=3
j=0
count=0
common_set=[]
for i in range (0, total_feature):
if peptide_feature[i, 7]<RT_min:
continue
ftr_mz=peptide_feature[i, 0]
ftr_charge=peptide_feature[i, 3]
ftr_meanRT=peptide_feature[i, 13]
while (j<feature_list.shape[0] and round(feature_list[j, 1], mz_resolution)<round(ftr_mz-mz_tolerance, mz_resolution)):
j=j+1
found=0
j2=j
while (j2<feature_list.shape[0] and round(feature_list[j2, 1], mz_resolution)<=round(ftr_mz+mz_tolerance, mz_resolution)):
ftr_mz_mq=round(feature_list[j2, 1], mz_resolution)
d_1=abs(ftr_mz_mq-ftr_mz)
ftr_mz_avg=(ftr_mz_mq+ftr_mz)/2.0
d_2=(ftr_mz_avg*10)/10**6
# print(d_2)
if d_1<d_2: # 10ppm error tolerance accepted
mq_rt=round(feature_list[j2, 4], 2)
if round(ftr_meanRT-RT_tolerance, 2) <= mq_rt and mq_rt <= round(ftr_meanRT+RT_tolerance, 2):
if ftr_charge==int(feature_list[j2, 0]):
found=1
break
j2=j2+1
if found==1:
common_set.append((i, j2))
count=count+1
print(count)
peptide_feature[:, 15]=-1
for i in range (0, len(common_set)):
peptide_feature[common_set[i][0], 15]=common_set[i][1] #indexing starts from 0
logfile=open(path+'feature_list/'+dataname[data_index]+'_combineIsotopes_featureList.csv', 'wb')
np.savetxt(logfile, peptide_feature, delimiter=',')
logfile.close()
f=open(path+'common_set/'+dataname[data_index]+'_common_set', 'wb')
pickle.dump(common_set, f, protocol=2)
f.close()