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PointIso_quantative_accuracy_csvlist.py
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from __future__ import division
from __future__ import print_function
import math
import csv
import pickle
import numpy as np
from collections import defaultdict
import scipy.stats
import gzip
import matplotlib.pyplot as plt
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_3_01','130124_dilA_3_02','130124_dilA_3_03','130124_dilA_3_04','130124_dilA_3_05','130124_dilA_3_06','130124_dilA_3_07',
'130124_dilA_4_01','130124_dilA_4_02','130124_dilA_4_03','130124_dilA_4_04','130124_dilA_4_05','130124_dilA_4_06','130124_dilA_4_07',
'130124_dilA_5_01','130124_dilA_5_02', '130124_dilA_5_03', '130124_dilA_5_04',
'130124_dilA_6_01','130124_dilA_6_02', '130124_dilA_6_03', '130124_dilA_6_04',
'130124_dilA_7_01','130124_dilA_7_02', '130124_dilA_7_03', '130124_dilA_7_04',
'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']
###########
choice = 1 #(1 for human, 2 for potato, 3 for background)
mappath='/data/fzohora/dilution_series_syn_pep/LC_MS/'
datapath='/data/fzohora/dilution_series_syn_pep/'
peaks_filename ='/data/fzohora/dilution_series_syn_pep/feature_list/PEAKs/PEAKSX/'+dataname[test_index]+'_peptide_feature.csv'
openMS_file=datapath+'feature_list/openMS/'+dataname[test_index]+'_openMS_features'
mz_resolution=2
RT_unit=0.01
mz_unit=0.01
result_all=np.zeros((len(dataname), 12))
threshold_score=10.0
percent_feature=20
min_RT=10.00
avg_sen=np.zeros((10))
avg_set_count=0
filename ='/data/fzohora/dilution_series_syn_pep/feature_list/mascot/spiked_peptides_in_the_dilution_series.csv'
peptide_list = []
csvfile=open(filename, 'r')
csvreader = csv.reader(csvfile)
for row in csvreader:
peptide_list.append(row)
csvfile.close()
spiked_peptide_dict=dict()#defaultdict(list)
for i in range (1, 116):
spiked_peptide_dict[peptide_list[i][0]]='potato' #.append('potato') #108
for i in range (1, 159):
spiked_peptide_dict[peptide_list[i][1]]='human' #.append('human') #142
#for i in range (1, 30):
# spiked_peptide_dict[peptide_list[i][4]].append('background') #2425
#2584 proteins in total
pointIso_human_peptide_quantity=defaultdict(list)
pointIso_potato_peptide_quantity=defaultdict(list)
pointIso_background_peptide_quantity=defaultdict(list)
for test_index in range (0, 57):
# print(dataname[test_index])
###########################################################################
threshold_score=25
f=open(datapath+'feature_list/mascot/'+dataname[test_index]+'_mascot_db_search', 'rb')
peptide_mascot=pickle.load(f)
f.close()
temp_peptide_mascot=[]
temp_peptide_mascot.append(peptide_mascot[0])
for i in range (1, len(peptide_mascot)):
if float(peptide_mascot[i][4])<=threshold_score:
continue
if round(float(peptide_mascot[i][7]), 2) < min_RT:
continue
temp_peptide_mascot.append(peptide_mascot[i])
peptide_mascot=temp_peptide_mascot
temp_peptide_mascot=0
peptide_mascot[0].append('type')
count=np.zeros((6))
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
for i in range (1, len(peptide_mascot)):
if peptide_mascot[i][5] in spiked_peptide_dict:
peptide_mascot[i].append(spiked_peptide_dict[peptide_mascot[i][5]])
if spiked_peptide_dict[peptide_mascot[i][5]]=='potato':
count[0]=count[0]+1
potato_detected_dict[peptide_mascot[i][5]]='found'
elif spiked_peptide_dict[peptide_mascot[i][5]]=='human' :
count[1]=count[1]+1
human_detected_dict[peptide_mascot[i][5]]='found'
else:
peptide_mascot[i].append('background')
background_detected_dict[peptide_mascot[i][5]]='found'
count[5]=count[5]+1
total_potato=len(potato_detected_dict.keys())
total_human=len(human_detected_dict.keys())
total_background=len(background_detected_dict.keys())
f=gzip.open(datapath+'/feature_list/deepIsoV2_'+dataname[test_index]+'_featureTable_v6r1_cv5_ev2r6b_merged_auc_exact_mz_fullRT','rb') #[-1.43,2.44] human-SRM
feature_table,auc_list=pickle.load(f)
f.close()
pointIso_feature_list_file='/home/fzohora/bsi/feature_list_csv_'+dataname[test_index]+'_feature_list_brief.csv'
feature_list=[]
with open(pointIso_feature_list_file) as file:
csv_file = csv.reader(file, delimiter=",")
for line in csv_file:
feature_list.append(line)
feature_table=defaultdict(list)
for i in range (1, len(feature_list)):
# m/z = [RT peak, charge, intensity, score, total isotopes]
feature_table[np.float(feature_list[i][0])].append([[np.float(feature_list[i][1]), np.float(feature_list[i][2]), np.float(feature_list[i][5], np.float(feature_list[i][6]), np.float(feature_list[i][7])]])
####################################################
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
RT_tolerance=0.2 # dino is same
found_ftr=0
total_feature=0
found_score=[]
for i in range (1, len(peptide_mascot)):
detected_ftr_list.append([])
found=0
mz_exact=round(float(peptide_mascot[i][2]), mz_resolution)
total_feature=total_feature+1
mz_range=[]
mz_range.append(mz_exact)
tolerance_mz=0.01 #(mz_exact*10.0)/10**6 # dinosaur = 0.005
mz_range.append(round(mz_exact-tolerance_mz, mz_resolution))
mz_range.append(round(mz_exact+tolerance_mz, mz_resolution))
pep_seq_key=str(test_index)+'-'+str(mz_exact)+str(round(float(peptide_mascot[i][7]), 2))+'|'+str(int(peptide_mascot[i][3]))+peptide_mascot[i][5]
for j in range (0, len(mz_range)):
mz=mz_range[j]
if mz in feature_table:
ftr_list=feature_table[mz]
for k in range (0, len(ftr_list)):
ftr=ftr_list[k]
ftr_z=int(ftr[1])
peak_RT=ftr[0]
score_ftr=np.max(ftr[3])
if (np.argmax(ftr[3])==1 and score_ftr<.80) or (ftr[4]==1 and score_ftr<.50) or score_ftr<.30:
continue
if (round(float(peptide_mascot[i][7])-RT_tolerance, 2) <= peak_RT) and (peak_RT<=round(float(peptide_mascot[i][7])+RT_tolerance, 2)) and ftr_z==int(peptide_mascot[i][3]):
found=1
found_ftr=found_ftr+1
detected_peptide[i, 0]=1
if peptide_mascot[i][8]=='potato':
potato_detected_dict[peptide_mascot[i][5]]='found'
pointIso_potato_peptide_quantity[pep_seq_key].append(ftr[2])
elif peptide_mascot[i][8]=='human':
human_detected_dict[peptide_mascot[i][5]]='found'
pointIso_human_peptide_quantity[pep_seq_key].append(ftr[2])
else:
background_detected_dict[peptide_mascot[i][5]]='found'
pointIso_background_peptide_quantity[pep_seq_key].append(ftr[2])
# break
# if found==1:
# break
#
# print('%s, %d, %g, %g, %d, %g, %g, %d'%(dataname[test_index], len(list(human_detected_dict.keys())), (len(list(human_detected_dict.keys()))/158)*100,
# (total_human/158)*100, len(list(potato_detected_dict.keys())), (len(list(potato_detected_dict.keys()))/115)*100, (total_potato/115)*100, total_report[0, 3]))
# print('%g'%((len(list(background_detected_dict.keys()))/total_background)*100))
# print('%s, %g, %d'%(dataname[test_index], (len(list(background_detected_dict.keys()))/total_background)*100, total_report[0, 3]))
if choice == 1:
candidate_peptide_quantity=pointIso_human_peptide_quantity
elif choice == 2:
candidate_peptide_quantity=pointIso_potato_peptide_quantity
elif choice == 3:
candidate_peptide_quantity=pointIso_background_peptide_quantity
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for pep_seq_key in candidate_peptide_quantity.keys():
# print(candidate_peptide_quantity[pep_seq_key])
intensity=np.mean(candidate_peptide_quantity[pep_seq_key]) #sum,avg,max? # multiple hit to the same psm
candidate_peptide_quantity[pep_seq_key]=intensity
ms_file=int((pep_seq_key.split('-'))[0])
mz_rt_z_seq=(pep_seq_key.split('-'))[1]
if ms_file>=53:
candidate_peptide_quantity_sample[11][mz_rt_z_seq].append(intensity)
elif ms_file>=49:
candidate_peptide_quantity_sample[10][mz_rt_z_seq].append(intensity)
elif ms_file>=45:
candidate_peptide_quantity_sample[9][mz_rt_z_seq].append(intensity)
elif ms_file>=41:
candidate_peptide_quantity_sample[8][mz_rt_z_seq].append(intensity)
elif ms_file>=37:
candidate_peptide_quantity_sample[7][mz_rt_z_seq].append(intensity)
elif ms_file>=33:
candidate_peptide_quantity_sample[6][mz_rt_z_seq].append(intensity)
elif ms_file>=29:
candidate_peptide_quantity_sample[5][mz_rt_z_seq].append(intensity)
elif ms_file>=25:
candidate_peptide_quantity_sample[4][mz_rt_z_seq].append(intensity)
elif ms_file>=18:
candidate_peptide_quantity_sample[3][mz_rt_z_seq].append(intensity)
elif ms_file>=11:
candidate_peptide_quantity_sample[2][mz_rt_z_seq].append(intensity)
elif ms_file>=4:
candidate_peptide_quantity_sample[1][mz_rt_z_seq].append(intensity)
elif ms_file>=0:
candidate_peptide_quantity_sample[0][mz_rt_z_seq].append(intensity)
temp_hold=candidate_peptide_quantity_sample
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for sample_id in range (0, 12):
feature_list=temp_hold[sample_id].keys()
for feature in feature_list:
considered_intensity = np.max(temp_hold[sample_id][feature]) # alignment over runs
seq=(feature.split('|'))[1]
if considered_intensity>0:
candidate_peptide_quantity_sample[sample_id][seq].append(considered_intensity)
temp_hold=candidate_peptide_quantity_sample
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for sample_id in range (0, 12):
feature_list=temp_hold[sample_id].keys()
for feature in feature_list:
considered_intensity = np.max(temp_hold[sample_id][feature]) # z,seq
seq=feature[1:len(feature)]
z=int(feature[0])
if considered_intensity>0:
candidate_peptide_quantity_sample[sample_id][seq].append(considered_intensity)
feature_auc_per_sample=defaultdict(list)
total_intensity=0
for sample_id in range (0, 12):
feature_list=candidate_peptide_quantity_sample[sample_id].keys()
for feature in feature_list:
considered_intensity = np.sum(candidate_peptide_quantity_sample[sample_id][feature]) # seq
if considered_intensity>0:
feature_auc_per_sample[feature].append([sample_id, considered_intensity])
total_intensity=total_intensity+considered_intensity
peptide_slopes=[]
feature_list=list(feature_auc_per_sample.keys())
for j in range(0, len(feature_list)):
feature=feature_list[j]
sample_list=feature_auc_per_sample[feature]
if len(sample_list)<2:
continue
#calculate slope
x_series=[]
y_series=[]
for i in range (0, len(sample_list)):
x_series.append(sample_list[i][0])
y_series.append(sample_list[i][1])
for i in range (0, len(y_series)):
y_series[i]=math.log(y_series[i]) #(y_series[i]-min_intensity)/(max_intensity-min_intensity)
# print(j)
# print(y_series)
slope, intercept, r, p, se = scipy.stats.linregress(x_series, y_series) #scipy.stats.linregress(x_series[start_file:end_file], y_series[start_file:end_file])
peptide_slopes.append([round(slope,2), feature])
slope_dist_pointIso=[]
peptide_slopes_pointIso=sorted(peptide_slopes)
for i in range (0, len(peptide_slopes)):
slope_dist_pointIso.append(peptide_slopes_pointIso[i][0])
plt.hist(slope_dist_pointIso, bins=10, alpha=0.9, density=True, label='pointIso', histtype='step') #
plt.legend(loc='upper right')
plt.show(block=False)
######################################################################################################
peaks_background_peptide_quantity=defaultdict(list)
peaks_human_peptide_quantity=defaultdict(list)
peaks_potato_peptide_quantity=defaultdict(list)
for test_index in range (0, 57):
filename ='/data/fzohora/dilution_series_syn_pep/feature_list/mascot/peptides_in_the_dilution_series.csv'
peptide_list = []
csvfile=open(filename, 'r')
csvreader = csv.reader(csvfile)
for row in csvreader:
peptide_list.append(row)
csvfile.close()
threshold_score=25
f=open(datapath+'feature_list/mascot/'+dataname[test_index]+'_mascot_db_search', 'rb')
peptide_mascot=pickle.load(f)
f.close()
#
temp_peptide_mascot=[]
temp_peptide_mascot.append(peptide_mascot[0])
for i in range (1, len(peptide_mascot)):
if float(peptide_mascot[i][4])<=threshold_score:
continue
if round(float(peptide_mascot[i][7]), 2) < min_RT:# or round(float(peptide_mascot[i][2]), mz_resolution)<min_mz: #or round(float(peptide_mascot[i][2]), mz_resolution)>800 or
continue
temp_peptide_mascot.append(peptide_mascot[i])
peptide_mascot=temp_peptide_mascot
temp_peptide_mascot=0
peptide_mascot[0].append('type')
count=np.zeros((6))
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
for i in range (1, len(peptide_mascot)):
if peptide_mascot[i][5] in spiked_peptide_dict:
peptide_mascot[i].append(spiked_peptide_dict[peptide_mascot[i][5]])
if spiked_peptide_dict[peptide_mascot[i][5]]=='potato':
count[0]=count[0]+1
potato_detected_dict[peptide_mascot[i][5]]='found'
elif spiked_peptide_dict[peptide_mascot[i][5]]=='human' :
count[1]=count[1]+1
human_detected_dict[peptide_mascot[i][5]]='found'
else:
peptide_mascot[i].append('background')
background_detected_dict[peptide_mascot[i][5]]='found'
count[5]=count[5]+1
total_potato=len(potato_detected_dict.keys())
total_human=len(human_detected_dict.keys())
total_background=len(background_detected_dict.keys())
##########################PEAKS###################################
#logfile=open(datapath+'feature_list/'+dataname[test_index]+'_combineIsotopes_featureList.csv', 'rb')
#peptide_feature=np.loadtxt(logfile, delimiter=',')
#logfile.close()
# initializing the titles and peptide_mascot list
peptide_feature = []
# reading csv file
csvfile=open(peaks_filename, 'r')
# creating a csv reader object
csvreader = csv.reader(csvfile)
# extracting each data row one by one
for row in csvreader:
peptide_feature.append(row)
csvfile.close()
feature_table_peaks=defaultdict(list)
# auc_list_peaks=[]
auc_dict_peaks=defaultdict(list)
for i in range (1, len(peptide_feature)):
if round(float(peptide_feature[i][4]), 2)<min_RT: # or round(peptide_feature[i, 0], mz_resolution) < min_mz: # or round(peptide_feature[i, 0], mz_resolution)>800:
continue
new_ftr=[]
new_ftr.append(round(float(peptide_feature[i][2]), mz_resolution))
new_ftr.append( round(float(peptide_feature[i][4]), 2)) #RT
new_ftr.append( int(float(peptide_feature[i][3]))) #charge
new_ftr.append(float(peptide_feature[i][7]))
new_ftr.append(i)
auc_dict_peaks[round(float(peptide_feature[i][7]))].append(i) # auc
feature_table_peaks[round(float(peptide_feature[i][2]), mz_resolution)].append(new_ftr)
total_report[0, 4]=total_report[0, 4]+1
auc_list_peaks=sorted(list(auc_dict_peaks.keys()), reverse=True)
RT_tolerance=0.2 # dino is same
found_ftr=0
total_feature=0
detected_peptide[:, 1]=0
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
for i in range (1, len(peptide_mascot)):
total_feature=total_feature+1
mz_exact=round(float(peptide_mascot[i][2]), mz_resolution)
mz_range=[]
mz_range.append(mz_exact)
tolerance_mz=0.01 #(mz_exact*10.0)/10**6 # dinosaur = 0.005
mz_range.append(round(mz_exact-tolerance_mz, mz_resolution))
mz_range.append(round(mz_exact+tolerance_mz, mz_resolution))
found=0
pep_seq_key=str(test_index)+'-'+str(mz_exact)+str(round(float(peptide_mascot[i][7]), 2))+'|'+str(int(peptide_mascot[i][3]))+peptide_mascot[i][5]
for j in range (0, len(mz_range)):
mz=mz_range[j]
if mz in feature_table_peaks:
ftr_list=feature_table_peaks[mz]
for k in range (0, len(ftr_list)):
ftr=ftr_list[k]
# if ftr[3]<auc_list_peaks[high_conf_limit]:
# continue
peak_RT=ftr[1]
if (round(float(peptide_mascot[i][7])-RT_tolerance, 2) <= peak_RT) and (peak_RT<=round(float(peptide_mascot[i][7])+RT_tolerance, 2)) and int(ftr[2])==int(peptide_mascot[i][3]):
found=1
found_ftr=found_ftr+1
if peptide_mascot[i][8]=='potato': #'potato-SRM':
potato_detected_dict[peptide_mascot[i][5]]='found'
peaks_potato_peptide_quantity[pep_seq_key].append(ftr[3])
elif peptide_mascot[i][8]=='human': #-SRM
human_detected_dict[peptide_mascot[i][5]]='found'
peaks_human_peptide_quantity[pep_seq_key].append(ftr[3])
else:
background_detected_dict[peptide_mascot[i][5]]='found'
peaks_background_peptide_quantity[pep_seq_key].append(ftr[3])
# break
# if found==1:
# break
# print('%d'%len(list(human_detected_dict.keys())))
# print('%d'%len(list(potato_detected_dict.keys())))
#human peptides are grouped by sample 1 to 12
# print(' %g, %g'%((len(list(human_detected_dict.keys()))/158)*100, (len(list(potato_detected_dict.keys()))/115)*100))
print('%g'%((len(list(background_detected_dict.keys()))/total_background)*100))
if choice == 1:
candidate_peptide_quantity=peaks_human_peptide_quantity
elif choice == 2:
candidate_peptide_quantity=peaks_potato_peptide_quantity
elif choice == 3:
candidate_peptide_quantity=peaks_background_peptide_quantity
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for pep_seq_key in candidate_peptide_quantity.keys():
# print(candidate_peptide_quantity[pep_seq_key])
intensity=np.mean(candidate_peptide_quantity[pep_seq_key]) #sum,avg,max? # multiple hit to the same psm
candidate_peptide_quantity[pep_seq_key]=intensity
ms_file=int((pep_seq_key.split('-'))[0])
mz_rt_z_seq=(pep_seq_key.split('-'))[1]
if ms_file>=53:
candidate_peptide_quantity_sample[11][mz_rt_z_seq].append(intensity)
elif ms_file>=49:
candidate_peptide_quantity_sample[10][mz_rt_z_seq].append(intensity)
elif ms_file>=45:
candidate_peptide_quantity_sample[9][mz_rt_z_seq].append(intensity)
elif ms_file>=41:
candidate_peptide_quantity_sample[8][mz_rt_z_seq].append(intensity)
elif ms_file>=37:
candidate_peptide_quantity_sample[7][mz_rt_z_seq].append(intensity)
elif ms_file>=33:
candidate_peptide_quantity_sample[6][mz_rt_z_seq].append(intensity)
elif ms_file>=29:
candidate_peptide_quantity_sample[5][mz_rt_z_seq].append(intensity)
elif ms_file>=25:
candidate_peptide_quantity_sample[4][mz_rt_z_seq].append(intensity)
elif ms_file>=18:
candidate_peptide_quantity_sample[3][mz_rt_z_seq].append(intensity)
elif ms_file>=11:
candidate_peptide_quantity_sample[2][mz_rt_z_seq].append(intensity)
elif ms_file>=4:
candidate_peptide_quantity_sample[1][mz_rt_z_seq].append(intensity)
elif ms_file>=0:
candidate_peptide_quantity_sample[0][mz_rt_z_seq].append(intensity)
temp_hold=candidate_peptide_quantity_sample
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for sample_id in range (0, 12):
feature_list=temp_hold[sample_id].keys()
for feature in feature_list:
considered_intensity = np.max(temp_hold[sample_id][feature]) # alignment over runs
seq=(feature.split('|'))[1]
if considered_intensity>0:
candidate_peptide_quantity_sample[sample_id][seq].append(considered_intensity)
temp_hold=candidate_peptide_quantity_sample
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for sample_id in range (0, 12):
feature_list=temp_hold[sample_id].keys()
for feature in feature_list:
considered_intensity = np.max(temp_hold[sample_id][feature]) # z,seq
seq=feature[1:len(feature)]
z=int(feature[0])
if considered_intensity>0:
candidate_peptide_quantity_sample[sample_id][seq].append(considered_intensity)
feature_auc_per_sample=defaultdict(list)
total_intensity=0
for sample_id in range (0, 12):
feature_list=candidate_peptide_quantity_sample[sample_id].keys()
for feature in feature_list:
considered_intensity = np.sum(candidate_peptide_quantity_sample[sample_id][feature]) # seq
if considered_intensity>0:
feature_auc_per_sample[feature].append([sample_id, considered_intensity])
total_intensity=total_intensity+considered_intensity
peptide_slopes=[]
feature_list=list(feature_auc_per_sample.keys())
for j in range(0, len(feature_list)):
feature=feature_list[j]
sample_list=feature_auc_per_sample[feature]
if len(sample_list)<2:
continue
#calculate slope
x_series=[]
y_series=[]
for i in range (0, len(sample_list)):
x_series.append(sample_list[i][0])
y_series.append(sample_list[i][1])
for i in range (0, len(y_series)):
y_series[i]=math.log(y_series[i]) #(y_series[i]-min_intensity)/(max_intensity-min_intensity)
# print(j)
# print(y_series)
slope, intercept, r, p, se = scipy.stats.linregress(x_series, y_series) #scipy.stats.linregress(x_series[start_file:end_file], y_series[start_file:end_file])
peptide_slopes.append([round(slope,2), feature])
slope_dist_peaks=[]
peptide_slopes_peaks=sorted(peptide_slopes)
for i in range (0, len(peptide_slopes)):
slope_dist_peaks.append(peptide_slopes_peaks[i][0])
plt.hist(slope_dist_peaks, bins=10, alpha=0.9, density=True, label='peaks', histtype='step') #
plt.legend(loc='upper right')
plt.show(block=False)
#############################################################################################
dino_human_peptide_quantity=defaultdict(list)
dino_potato_peptide_quantity=defaultdict(list)
dino_background_peptide_quantity=defaultdict(list)
for test_index in range (0, 57):
for runtime in range (0, 1): #
filename ='/data/fzohora/dilution_series_syn_pep/feature_list/mascot/peptides_in_the_dilution_series.csv'
peptide_list = []
csvfile=open(filename, 'r')
csvreader = csv.reader(csvfile)
for row in csvreader:
peptide_list.append(row)
csvfile.close()
threshold_score=25
f=open(datapath+'feature_list/mascot/'+dataname[test_index]+'_mascot_db_search', 'rb')
peptide_mascot=pickle.load(f)
f.close()
#
temp_peptide_mascot=[]
temp_peptide_mascot.append(peptide_mascot[0])
for i in range (1, len(peptide_mascot)):
if float(peptide_mascot[i][4])<=threshold_score:
continue
if round(float(peptide_mascot[i][7]), 2) < min_RT:# or round(float(peptide_mascot[i][2]), mz_resolution)<min_mz: #or round(float(peptide_mascot[i][2]), mz_resolution)>800 or
continue
temp_peptide_mascot.append(peptide_mascot[i])
peptide_mascot=temp_peptide_mascot
temp_peptide_mascot=0
total_report=np.zeros((1, 5)) # 0 = openMS, 1=maxQuant, 2=dino, 3=DeepIso, 4= peaks
ftr_matched_auc=np.zeros((len(peptide_mascot), 5)) # 0 = openMS, 1=maxQuant, 2=dino, 3=DeepIso, 4= peaks
detected_peptide=np.zeros((len(peptide_mascot), 7)) # 0 = our, 1 = peaks, 2 = maxquant, 3= charge by Peaks, 4=peaks id, 5=dino, 6=openMS
peptide_mascot[0].append('type')
count=np.zeros((6))
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
for i in range (1, len(peptide_mascot)):
if peptide_mascot[i][5] in spiked_peptide_dict:
peptide_mascot[i].append(spiked_peptide_dict[peptide_mascot[i][5]])
if spiked_peptide_dict[peptide_mascot[i][5]]=='potato':
count[0]=count[0]+1
potato_detected_dict[peptide_mascot[i][5]]='found'
elif spiked_peptide_dict[peptide_mascot[i][5]]=='human' :
count[1]=count[1]+1
human_detected_dict[peptide_mascot[i][5]]='found'
else:
peptide_mascot[i].append('background')
background_detected_dict[peptide_mascot[i][5]]='found'
count[5]=count[5]+1
total_potato=len(potato_detected_dict.keys())
total_human=len(human_detected_dict.keys())
total_background=len(background_detected_dict.keys())
########### Dinosaurs ################################################
# filename ='/data/fzohora/dilution_series_syn_pep/feature_list/dino/'+dataname[test_index]+'_dino.csv'
dino_peptide_mascot = []
try:
dino_filename ='/data/fzohora/dilution_series_syn_pep/feature_list/dino/'+dataname[test_index]+'.features.tsv'
# reading csv file
csvfile=open(dino_filename, 'r')
# creating a csv reader object
csvreader = csv.reader(csvfile, delimiter='\t')
# extracting each data row one by one
for row in csvreader:
dino_peptide_mascot.append(row)
csvfile.close()
except:
# if test_index in (36, 43, 44):
filename ='/data/fzohora/dilution_series_syn_pep/feature_list/dino/'+dataname[test_index]+'_dino.csv'
# reading csv file
csvfile=open(filename, 'r')
# creating a csv reader object
csvreader = csv.reader(csvfile, delimiter=',')
# extracting each data row one by one
for row in csvreader:
dino_peptide_mascot.append(row)
csvfile.close()
feature_table_dino=defaultdict(list)
auc_list_dino=[]
auc_dict_dino=defaultdict(list)
count=0
for i in range (1, len(dino_peptide_mascot)):
if float(dino_peptide_mascot[i][3])<min_RT: #or round(float(dino_peptide_mascot[i][0]), mz_resolution)<min_mz: # or round(float(dino_peptide_mascot[i][0]), mz_resolution)>800:
continue
new_ftr=[]
new_ftr.append(round(float(dino_peptide_mascot[i][0]), mz_resolution))
new_ftr.append(round(float(float(dino_peptide_mascot[i][4])), 2))
try:
new_ftr.append(float(dino_peptide_mascot[i][13]))
auc_dict_dino[round(float(dino_peptide_mascot[i][13]))].append(i)
except:
count=count+1
new_ftr.append(0)
auc_dict_dino[0].append(i)
new_ftr.append(dino_peptide_mascot[i][2]) #charge
feature_table_dino[round(float(dino_peptide_mascot[i][0]), mz_resolution)].append(new_ftr)
auc_list_dino=sorted(list(auc_dict_dino.keys()), reverse=True)
# print('probb %d'%count)
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
RT_tolerance=0.2 # dino is same
found_ftr=0
total_feature=0
detected_peptide[:, 5]=0
for i in range (1, len(peptide_mascot)):
total_feature=total_feature+1
mz_exact=round(float(peptide_mascot[i][2]), mz_resolution)
mz_range=[]
mz_range.append(mz_exact)
tolerance_mz=0.01 #(mz_exact*10.0)/10**6 # dinosaur = 0.005
mz_range.append(round(mz_exact-tolerance_mz, mz_resolution))
mz_range.append(round(mz_exact+tolerance_mz, mz_resolution))
found=0
pep_seq_key=str(test_index)+'-'+str(mz_exact)+str(round(float(peptide_mascot[i][7]), 2))+'|'+str(int(peptide_mascot[i][3]))+peptide_mascot[i][5]
for j in range (0, len(mz_range)):
mz=mz_range[j]
if mz in feature_table_dino:
ftr_list=feature_table_dino[mz]
for k in range (0, len(ftr_list)):
ftr=ftr_list[k]
# if ftr[2]<auc_list_dino[high_conf_limit]:
# continue
peak_RT=ftr[1]
if (round(float(peptide_mascot[i][7])-RT_tolerance, 2) <= peak_RT) and (peak_RT<=round(float(peptide_mascot[i][7])+RT_tolerance, 2))and int(ftr[3])==int(peptide_mascot[i][3]):
found=1
detected_peptide[i, 5]=1
found_ftr=found_ftr+1
ftr_matched_auc[i, 2]=ftr[2]
if peptide_mascot[i][8]=='potato': #-SRM
potato_detected_dict[peptide_mascot[i][5]]='found'
dino_potato_peptide_quantity[pep_seq_key].append(ftr[2])
elif peptide_mascot[i][8]=='human': #-SRM
human_detected_dict[peptide_mascot[i][5]]='found'
dino_human_peptide_quantity[pep_seq_key].append(ftr[2])
else:
background_detected_dict[peptide_mascot[i][5]]='found'
dino_background_peptide_quantity[pep_seq_key].append(ftr[2])
# break
#
# if found==1:
# break
print('%g'%((len(list(background_detected_dict.keys()))/total_background)*100))
# print(' %g, %g'%((len(list(human_detected_dict.keys()))/158)*100, (len(list(potato_detected_dict.keys()))/115)*100))
if choice == 1:
candidate_peptide_quantity=dino_human_peptide_quantity
elif choice == 2:
candidate_peptide_quantity=dino_potato_peptide_quantity
elif choice == 3:
candidate_peptide_quantity=dino_background_peptide_quantity
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for pep_seq_key in candidate_peptide_quantity.keys():
# print(candidate_peptide_quantity[pep_seq_key])
intensity=np.mean(candidate_peptide_quantity[pep_seq_key]) #sum,avg,max? # multiple hit to the same psm
candidate_peptide_quantity[pep_seq_key]=intensity
ms_file=int((pep_seq_key.split('-'))[0])
mz_rt_z_seq=(pep_seq_key.split('-'))[1]
if ms_file>=53:
candidate_peptide_quantity_sample[11][mz_rt_z_seq].append(intensity)
elif ms_file>=49:
candidate_peptide_quantity_sample[10][mz_rt_z_seq].append(intensity)
elif ms_file>=45:
candidate_peptide_quantity_sample[9][mz_rt_z_seq].append(intensity)
elif ms_file>=41:
candidate_peptide_quantity_sample[8][mz_rt_z_seq].append(intensity)
elif ms_file>=37:
candidate_peptide_quantity_sample[7][mz_rt_z_seq].append(intensity)
elif ms_file>=33:
candidate_peptide_quantity_sample[6][mz_rt_z_seq].append(intensity)
elif ms_file>=29:
candidate_peptide_quantity_sample[5][mz_rt_z_seq].append(intensity)
elif ms_file>=25:
candidate_peptide_quantity_sample[4][mz_rt_z_seq].append(intensity)
elif ms_file>=18:
candidate_peptide_quantity_sample[3][mz_rt_z_seq].append(intensity)
elif ms_file>=11:
candidate_peptide_quantity_sample[2][mz_rt_z_seq].append(intensity)
elif ms_file>=4:
candidate_peptide_quantity_sample[1][mz_rt_z_seq].append(intensity)
elif ms_file>=0:
candidate_peptide_quantity_sample[0][mz_rt_z_seq].append(intensity)
temp_hold=candidate_peptide_quantity_sample
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for sample_id in range (0, 12):
feature_list=temp_hold[sample_id].keys()
for feature in feature_list:
considered_intensity = np.max(temp_hold[sample_id][feature]) # alignment over runs
seq=(feature.split('|'))[1]
if considered_intensity>0:
candidate_peptide_quantity_sample[sample_id][seq].append(considered_intensity)
temp_hold=candidate_peptide_quantity_sample
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for sample_id in range (0, 12):
feature_list=temp_hold[sample_id].keys()
for feature in feature_list:
considered_intensity = np.max(temp_hold[sample_id][feature]) # z,seq
seq=feature[1:len(feature)]
z=int(feature[0])
if considered_intensity>0:
candidate_peptide_quantity_sample[sample_id][seq].append(considered_intensity)
feature_auc_per_sample=defaultdict(list)
total_intensity=0
for sample_id in range (0, 12):
feature_list=candidate_peptide_quantity_sample[sample_id].keys()
for feature in feature_list:
considered_intensity = np.sum(candidate_peptide_quantity_sample[sample_id][feature]) # seq
if considered_intensity>0:
feature_auc_per_sample[feature].append([sample_id, considered_intensity])
total_intensity=total_intensity+considered_intensity
peptide_slopes=[]
feature_list=list(feature_auc_per_sample.keys())
for j in range(0, len(feature_list)):
feature=feature_list[j]
sample_list=feature_auc_per_sample[feature]
if len(sample_list)<2:
continue
#calculate slope
x_series=[]
y_series=[]
for i in range (0, len(sample_list)):
x_series.append(sample_list[i][0])
y_series.append(sample_list[i][1])
for i in range (0, len(y_series)):
y_series[i]=math.log(y_series[i]) #(y_series[i]-min_intensity)/(max_intensity-min_intensity)
# print(j)
# print(y_series)
slope, intercept, r, p, se = scipy.stats.linregress(x_series, y_series) #scipy.stats.linregress(x_series[start_file:end_file], y_series[start_file:end_file])
peptide_slopes.append([round(slope,2), feature])
slope_dist_dino=[]
peptide_slopes_dino=sorted(peptide_slopes)
for i in range (0, len(peptide_slopes)):
slope_dist_dino.append(peptide_slopes_dino[i][0])
plt.hist(slope_dist_dino, bins=10, alpha=0.9, density=True, label='dino', histtype='step') #
plt.legend(loc='upper right')
plt.show(block=False)
##############################################################################################################
mq_background_peptide_quantity=defaultdict(list)
mq_human_peptide_quantity=defaultdict(list)
mq_potato_peptide_quantity=defaultdict(list)
for test_index in range (0, 57):
for runtime in range (0, 1): #
filename ='/data/fzohora/dilution_series_syn_pep/feature_list/mascot/peptides_in_the_dilution_series.csv'
peptide_list = []
csvfile=open(filename, 'r')
csvreader = csv.reader(csvfile)
for row in csvreader:
peptide_list.append(row)
csvfile.close()
threshold_score=25
f=open(datapath+'feature_list/mascot/'+dataname[test_index]+'_mascot_db_search', 'rb')
peptide_mascot=pickle.load(f)
f.close()
#
temp_peptide_mascot=[]
temp_peptide_mascot.append(peptide_mascot[0])
for i in range (1, len(peptide_mascot)):
if float(peptide_mascot[i][4])<=threshold_score:
continue
if round(float(peptide_mascot[i][7]), 2) < min_RT:# or round(float(peptide_mascot[i][2]), mz_resolution)<min_mz: #or round(float(peptide_mascot[i][2]), mz_resolution)>800 or
continue
temp_peptide_mascot.append(peptide_mascot[i])
peptide_mascot=temp_peptide_mascot
temp_peptide_mascot=0
peptide_mascot[0].append('type')
count=np.zeros((6))
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
for i in range (1, len(peptide_mascot)):
if peptide_mascot[i][5] in spiked_peptide_dict:
peptide_mascot[i].append(spiked_peptide_dict[peptide_mascot[i][5]])
if spiked_peptide_dict[peptide_mascot[i][5]]=='potato':
count[0]=count[0]+1
potato_detected_dict[peptide_mascot[i][5]]='found'
elif spiked_peptide_dict[peptide_mascot[i][5]]=='human' :
count[1]=count[1]+1
human_detected_dict[peptide_mascot[i][5]]='found'
else:
peptide_mascot[i].append('background')
background_detected_dict[peptide_mascot[i][5]]='found'
count[5]=count[5]+1
total_potato=len(potato_detected_dict.keys())
total_human=len(human_detected_dict.keys())
total_background=len(background_detected_dict.keys())
######################################################################
MQ_filename ='/data/fzohora/dilution_series_syn_pep/feature_list/maxQ/'+dataname[test_index]+'_3.csv'
# initializing the titles and peptide_mascot list
MQ_peptide_mascot = []
# reading csv file
csvfile=open(MQ_filename, 'r')
# creating a csv reader object
csvreader = csv.reader(csvfile)
# extracting each data row one by one
for row in csvreader:
MQ_peptide_mascot.append(row)
csvfile.close()
auc_dict_MQ=defaultdict(list)
feature_table_mq=defaultdict(list)
auc_list_MQ=[]
count=0
for i in range (1, len(MQ_peptide_mascot)):
if float(MQ_peptide_mascot[i][4])<min_RT: # or round(float(MQ_peptide_mascot[i][1]), mz_resolution)<min_mz: #or round(float(MQ_peptide_mascot[i][1]), mz_resolution)>800 :
continue
new_ftr=[]
new_ftr.append(round(float(MQ_peptide_mascot[i][1]), mz_resolution))
new_ftr.append(round(float(float(MQ_peptide_mascot[i][4])), 2))
try:
new_ftr.append(float(MQ_peptide_mascot[i][5]))
auc_dict_MQ[round(float(MQ_peptide_mascot[i][5]))].append(i)
except:
count=count+1
new_ftr.append(0)
auc_dict_MQ[0].append(i)
new_ftr.append(MQ_peptide_mascot[i][0]) #charge
feature_table_mq[round(float(MQ_peptide_mascot[i][1]), mz_resolution)].append(new_ftr)
auc_list_MQ=sorted(list(auc_dict_MQ.keys()), reverse=True)
# print('probb %d'%count)
potato_detected_dict=dict()
human_detected_dict=dict()
background_detected_dict=dict()
RT_tolerance=0.2 # dino is same
found_ftr=0
total_feature=0
detected_peptide[:, 2]=0
for i in range (1, len(peptide_mascot)):
# if float(peptide_mascot[i][7])<100000:
# continue
total_feature=total_feature+1
mz_exact=round(float(peptide_mascot[i][2]), mz_resolution)
mz_range=[]
mz_range.append(mz_exact)
tolerance_mz=0.01 #(mz_exact*10.0)/10**6 # dinosaur = 0.005
mz_range.append(round(mz_exact-tolerance_mz, mz_resolution))
mz_range.append(round(mz_exact+tolerance_mz, mz_resolution))
found=0
pep_seq_key=str(test_index)+'-'+str(mz_exact)+str(round(float(peptide_mascot[i][7]), 2))+'|'+str(int(peptide_mascot[i][3]))+peptide_mascot[i][5]
for j in range (0, len(mz_range)):
mz=mz_range[j]
if mz in feature_table_mq:
ftr_list=feature_table_mq[mz]
for k in range (0, len(ftr_list)):
ftr=ftr_list[k]
# if ftr[2]<auc_list_MQ[high_conf_limit]:
# continue
peak_RT=ftr[1]
if (round(float(peptide_mascot[i][7])-RT_tolerance, 2) <= peak_RT) and (peak_RT<=round(float(peptide_mascot[i][7])+RT_tolerance, 2)) and int(ftr[3])==int(peptide_mascot[i][3]):
found=1
detected_peptide[i, 2]=1
found_ftr=found_ftr+1
ftr_matched_auc[i, 1]=ftr[2]
if peptide_mascot[i][8]=='potato': #-SRM
potato_detected_dict[peptide_mascot[i][5]]='found'
mq_potato_peptide_quantity[pep_seq_key].append(ftr[2])
elif peptide_mascot[i][8]=='human': #-SRM
human_detected_dict[peptide_mascot[i][5]]='found'
mq_human_peptide_quantity[pep_seq_key].append(ftr[2])
else:
background_detected_dict[peptide_mascot[i][5]]='found'
mq_background_peptide_quantity[pep_seq_key].append(ftr[2])
# break
#
# if found==1:
# break
print('%g'%((len(list(background_detected_dict.keys()))/total_background)*100))
# print('%d'%len(list(human_detected_dict.keys())))
# print(' %g, %g'%((len(list(human_detected_dict.keys()))/158)*100, (len(list(potato_detected_dict.keys()))/115)*100))
if choice == 1:
candidate_peptide_quantity=mq_human_peptide_quantity
elif choice == 2:
candidate_peptide_quantity=mq_potato_peptide_quantity
elif choice == 3:
candidate_peptide_quantity=mq_background_peptide_quantity
candidate_peptide_quantity_sample=[]
for i in range (0, 12):
candidate_peptide_quantity_sample.append(defaultdict(list))
for pep_seq_key in candidate_peptide_quantity.keys():
# print(candidate_peptide_quantity[pep_seq_key])
intensity=np.mean(candidate_peptide_quantity[pep_seq_key]) #sum,avg,max? # multiple hit to the same psm
candidate_peptide_quantity[pep_seq_key]=intensity
ms_file=int((pep_seq_key.split('-'))[0])
mz_rt_z_seq=(pep_seq_key.split('-'))[1]
if ms_file>=53:
candidate_peptide_quantity_sample[11][mz_rt_z_seq].append(intensity)
elif ms_file>=49:
candidate_peptide_quantity_sample[10][mz_rt_z_seq].append(intensity)
elif ms_file>=45:
candidate_peptide_quantity_sample[9][mz_rt_z_seq].append(intensity)
elif ms_file>=41:
candidate_peptide_quantity_sample[8][mz_rt_z_seq].append(intensity)
elif ms_file>=37:
candidate_peptide_quantity_sample[7][mz_rt_z_seq].append(intensity)
elif ms_file>=33:
candidate_peptide_quantity_sample[6][mz_rt_z_seq].append(intensity)
elif ms_file>=29:
candidate_peptide_quantity_sample[5][mz_rt_z_seq].append(intensity)
elif ms_file>=25:
candidate_peptide_quantity_sample[4][mz_rt_z_seq].append(intensity)
elif ms_file>=18:
candidate_peptide_quantity_sample[3][mz_rt_z_seq].append(intensity)
elif ms_file>=11:
candidate_peptide_quantity_sample[2][mz_rt_z_seq].append(intensity)
elif ms_file>=4: