-
Notifications
You must be signed in to change notification settings - Fork 1
/
1_2_mol2net.py
262 lines (150 loc) · 7.57 KB
/
1_2_mol2net.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
#python3
'''
================================
1_2_mol2net -- MOLECULAR NETWORK
================================
AUTHOR: Alexander Ruf ([email protected])
If you use this code for your work, please cite the corresponding paper:
10.1021/acs.analchem.2c01271
, this github repository and/or the corresponding Zenodo DOI:
@software{rufalexan_2022_7025094,
author = {rufalexan},
title = {rufalexan/mol2net: v0.1.0},
month = aug,
year = 2022,
publisher = {Zenodo},
version = {v0.1.0},
doi = {10.5281/zenodo.7025094},
url = {https://doi.org/10.5281/zenodo.7025094}
}
'''
from numpy import *
import pandas as pd
import string
from collections import namedtuple
from scipy import stats
from itertools import combinations
import matplotlib as mpl
#from matplotlib import cm
#from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import datetime
begin_time = datetime.datetime.now()
################################
## DATA
################################
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##name = 'pubchem_database_CHNO_1-10000'
name = 'TEST_pubchem_database_CHNO_10001-20000'
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
matrix = pd.read_csv(name + '.tsv', sep='\t', skiprows=0).to_numpy()
##name = 'PubChem 1-10000' #################################
name = 'TEST_PubChem 10001-20000' #################################
id = around(matrix[:,0].reshape(len(matrix),1).astype(float),6)
mz = around(matrix[:,2].reshape(len(matrix),1).astype(float),6)
mz2 = mz
intensity = around(matrix[:,3].reshape(len(matrix),1).astype(float),6)
data = concatenate([mz, id, intensity], axis=1)
data2 = data
################################
## list match
################################
name_reactions = 'TRAFOS'
reactions_input = pd.read_csv(name_reactions + '.tsv', sep='\t', skiprows=0).to_numpy()
h = reactions_input[:,0]
c = reactions_input[:,1]
n = reactions_input[:,2]
o = reactions_input[:,3]
s = reactions_input[:,4]
m_h = 1.007825
m_c = 12.
m_o = 15.994915
m_n = 14.003074
m_s = 31.972071
masses = (m_h, m_c, m_n, m_o, m_s)
elements = transpose(array([h, c, n, o, s]))
mass_differences = around(array(abs(dot(elements, masses)).reshape(len(reactions_input),1), dtype=float),6)
def prepend(list, str):
str += '{0}'
list = [str.format(i) for i in list]
return(list)
hydrogen = 'H'
carbon = 'C'
nitrogen = 'N'
oxygen = 'O'
sulfur = 'S'
hydrogens = prepend(h, hydrogen)
carbons = prepend(c, carbon)
nitrogens = prepend(n, nitrogen)
oxygens = prepend(o, oxygen)
sulfurs = prepend(s, sulfur)
formulas = [str(carbons[i]) + str(hydrogens[i]) + str(nitrogens[i]) + str(oxygens[i]) + str(sulfurs[i]) + str(' ') for i in range(len(carbons))]
formulas = [sub.replace('C1H', 'CH') for sub in formulas]
formulas = [sub.replace('H1N', 'HN') for sub in formulas]
formulas = [sub.replace('N1O', 'NO') for sub in formulas]
formulas = [sub.replace('C0', '') for sub in formulas]
formulas = [sub.replace('H0', '') for sub in formulas]
formulas = [sub.replace('N0', '') for sub in formulas]
formulas = [sub.replace('O0', '') for sub in formulas]
formulas = [sub.replace('O1', 'O') for sub in formulas]
formulas = [sub.replace('S0', '') for sub in formulas]
formulas = [sub.replace('S1', 'S') for sub in formulas]
formulas = array(formulas).reshape(len(hydrogens),1)
reactions = concatenate([elements, mass_differences, formulas], axis=1)
header_reactions = (array(['H', 'C', 'N', 'O', 'S', 'mass_difference', 'molecular_formula'])).reshape(len(reactions.T),1).T
reactions2 = concatenate((header_reactions, reactions))
df_reactions = pd.DataFrame({'mass_difference': reactions[:, 5], 'molecular_formula': reactions[:, 6], 'H': reactions[:, 0], 'C': reactions[:, 1], 'N': reactions[:, 2], 'O': reactions[:, 3], 'S': reactions[:, 4]}).reindex(columns=(['molecular_formula', 'mass_difference', 'H', 'C', 'N', 'O', 'S']))
################################
## md matches
################################
md_matches = namedtuple('md_matches', 'md_matches hits')
new_list = []
for md in mass_differences:
for element in mz:
if element+md in mz2:
tmp_new_list = md_matches(element, element+md)
new_list.append(tmp_new_list)
matches = array(new_list).reshape(len(new_list),2)
matches = matches[argsort(matches[:,0])]
df_sources = pd.DataFrame({'mz_sources': data[:, 0], 'id_sources': data[:, 1]})
df_targets = pd.DataFrame({'mz_targets': data[:, 0], 'id_targets': data[:, 1]})
df_matches = pd.DataFrame({'mz_sources': matches[:, 0], 'mz_targets': matches[:, 1]})
df_source_match = df_matches.merge(df_sources, how='left', on=['mz_sources'])
df_target_match = df_source_match.merge(df_targets, how='left', on=['mz_targets'])
mass_diff = around(df_target_match['mz_targets'] - df_target_match['mz_sources'],6)
type = full((len(mass_diff),1), 'Undirected')
type = pd.DataFrame({'type': type[:, 0]})
Label = full((len(mass_diff),1), 'x')
Label = pd.DataFrame({'Label': Label[:, 0]})
Weight = full((len(mass_diff),1), 1)
Weight = pd.DataFrame({'Weight': Weight[:, 0]})
df_full_edge_matrix = pd.concat([df_target_match, type, Label, Weight, mass_diff.rename('mass_difference')], axis=1)
edge_list = ( df_full_edge_matrix.reindex(columns=['id_sources', 'id_targets', 'type', 'Weight', 'mz_sources', 'mz_targets', 'Label', 'mass_difference']) ).rename(columns={'id_sources': 'Source', 'id_targets': 'Target'})
df_mz_md = (pd.DataFrame({'mass_difference': reactions[:, 5], 'molecular_formula': reactions[:, 6]})).reindex(columns=['molecular_formula', 'mass_difference'])
df_mz_md['mass_difference'] = df_mz_md['mass_difference'].astype(str)
edge_list['mass_difference'] = edge_list['mass_difference'].astype(str)
df_edge_list = edge_list.merge(df_mz_md, how='left', on=['mass_difference'])
df_edge_list['Source'] = df_edge_list['Source'].astype(int)
df_edge_list['Target'] = df_edge_list['Target'].astype(int)
################################
## freq of mass differences
################################
freq_of_md = stats.itemfreq(mass_diff)
df_freq_of_md = pd.DataFrame({'mass_difference': freq_of_md[:, 0], 'freq_of_md': freq_of_md[:, 1]})
df_freq_of_md = df_freq_of_md.reindex(columns=['mass_difference', 'freq_of_md'])
df_freq_of_md['mass_difference'] = df_freq_of_md['mass_difference'].astype(str)
df_reactions['mass_difference'] = df_reactions['mass_difference'].astype(str)
df_reactions_freq = df_reactions.merge(df_freq_of_md, how='left', on=['mass_difference'])
#df_reactions_freq = pd.concat([df_reactions, df_freq_of_md], axis=1, sort=False)
df_reactions_freq.plot.bar(x='molecular_formula', y='freq_of_md')
################################
## export
################################
df_edge_list_gephi = df_edge_list
df_edge_list_gephi2 = df_edge_list_gephi[['Source', 'Target', 'type', 'mass_difference', 'mz_sources', 'mz_targets', 'Label', 'molecular_formula']].reindex(columns=['Source', 'Target', 'type', 'mass_difference', 'mz_sources', 'mz_targets', 'Label', 'molecular_formula']).rename(columns={'mass_difference':'Weight', 'mz_sources': 'mz_source', 'mz_targets':'mz_target'}) # Weights = MDs
formulas_list = formulas.tolist()
df_edge_list_gephi3 = df_edge_list_gephi.merge(df_freq_of_md, how='left', on=['mass_difference'])
df_edge_list_gephi3.to_csv('EDGES_' + name + str(formulas_list) + '.tsv', sep='\t', index=False) # with frequencies of MDs
print(name)
print(df_reactions_freq)
print('Time code = '+str(datetime.datetime.now() - begin_time)+' [h:min:sec]')