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
256
257
258
259
|
# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero General Public License for more details.
#
# This program is available from Source Forge: at GeneNetwork Project
# (sourceforge.net/projects/genenetwork/).
#
# Contact Dr. Robert W. Williams at rwilliams@uthsc.edu
#
#
# This module is used by GeneNetwork project (www.genenetwork.org)
import datetime
import random
import string
import numpy as np
import scipy
from gn2.base.data_set import create_dataset
from gn2.base.webqtlConfig import GENERATED_TEXT_DIR
from gn2.utility.helper_functions import get_trait_db_obs
from gn2.utility.corr_result_helpers import normalize_values
from gn2.utility.redis_tools import get_redis_conn
from gn3.computations.pca import compute_pca
from gn3.computations.pca import process_factor_loadings_tdata
from gn3.computations.pca import generate_pca_temp_traits
from gn3.computations.pca import cache_pca_dataset
from gn3.computations.pca import generate_scree_plot_data
class CorrelationMatrix:
def __init__(self, start_vars):
trait_db_list = [trait.strip()
for trait in start_vars['trait_list'].split(',')]
get_trait_db_obs(self, trait_db_list)
self.all_sample_list = []
self.traits = []
self.do_PCA = True
# ZS: Getting initial group name before verifying all traits are in the same group in the following loop
this_group = self.trait_list[0][1].group.name
for trait_db in self.trait_list:
this_group = trait_db[1].group.name
this_trait = trait_db[0]
self.traits.append(this_trait)
this_sample_data = this_trait.data
for sample in this_sample_data:
if sample not in self.all_sample_list:
self.all_sample_list.append(sample)
self.sample_data = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_sample_data = this_trait.data
this_trait_vals = []
for sample in self.all_sample_list:
if sample in this_sample_data:
this_trait_vals.append(this_sample_data[sample].value)
else:
this_trait_vals.append('')
self.sample_data.append(this_trait_vals)
# Shouldn't do PCA if there are more traits than observations/samples
if len(this_trait_vals) < len(self.trait_list) or len(self.trait_list) < 3:
self.do_PCA = False
# ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning)
self.lowest_overlap = 8
self.corr_results = []
self.pca_corr_results = []
self.scree_data = []
self.shared_samples_list = self.all_sample_list
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_db = trait_db[1]
this_db_samples = this_db.group.all_samples_ordered()
this_sample_data = this_trait.data
corr_result_row = []
pca_corr_result_row = []
is_spearman = False # ZS: To determine if it's above or below the diagonal
for target in self.trait_list:
target_trait = target[0]
target_db = target[1]
target_samples = target_db.group.all_samples_ordered()
target_sample_data = target_trait.data
this_trait_vals = []
target_vals = []
for index, sample in enumerate(target_samples):
if (sample in this_sample_data) and (sample in target_sample_data):
sample_value = this_sample_data[sample].value
target_sample_value = target_sample_data[sample].value
this_trait_vals.append(sample_value)
target_vals.append(target_sample_value)
else:
if sample in self.shared_samples_list:
self.shared_samples_list.remove(sample)
this_trait_vals, target_vals, num_overlap = normalize_values(
this_trait_vals, target_vals)
if num_overlap < self.lowest_overlap:
self.lowest_overlap = num_overlap
if num_overlap < 2:
corr_result_row.append([target_trait, 0, num_overlap])
pca_corr_result_row.append(0)
else:
pearson_r, pearson_p = scipy.stats.pearsonr(
this_trait_vals, target_vals)
if is_spearman == False:
sample_r, sample_p = pearson_r, pearson_p
if sample_r > 0.999:
is_spearman = True
else:
sample_r, sample_p = scipy.stats.spearmanr(
this_trait_vals, target_vals)
corr_result_row.append(
[target_trait, sample_r, num_overlap])
pca_corr_result_row.append(pearson_r)
self.corr_results.append(corr_result_row)
self.pca_corr_results.append(pca_corr_result_row)
self.export_filename, self.export_filepath = export_corr_matrix(
self.corr_results)
self.trait_data_array = []
for trait_db in self.trait_list:
this_trait = trait_db[0]
this_db = trait_db[1]
this_db_samples = this_db.group.all_samples_ordered()
this_sample_data = this_trait.data
this_trait_vals = []
for index, sample in enumerate(this_db_samples):
if (sample in this_sample_data) and (sample in self.shared_samples_list):
sample_value = this_sample_data[sample].value
this_trait_vals.append(sample_value)
self.trait_data_array.append(this_trait_vals)
groups = []
for sample in self.all_sample_list:
groups.append(1)
self.pca_works = "False"
try:
if self.do_PCA:
self.pca_works = "True"
self.pca_trait_ids = []
pca = self.calculate_pca()
self.loadings_array = process_factor_loadings_tdata(
factor_loadings=self.loadings, traits_num=len(self.trait_list))
else:
self.pca_works = "False"
except:
self.pca_works = "False"
self.js_data = dict(traits=[trait.name for trait in self.traits],
groups=groups,
scree_data = self.scree_data,
cols=list(range(len(self.traits))),
rows=list(range(len(self.traits))),
samples=self.all_sample_list,
sample_data=self.sample_data,)
def calculate_pca(self):
pca = compute_pca(self.pca_corr_results)
self.loadings = pca["components"]
self.scores = pca["scores"]
self.pca_obj = pca["pca"]
this_group_name = self.trait_list[0][1].group.name
temp_dataset = create_dataset(
dataset_name="Temp", dataset_type="Temp",
group_name=this_group_name)
temp_dataset.group.get_samplelist(redis_conn=get_redis_conn())
pca_temp_traits = generate_pca_temp_traits(species=temp_dataset.group.species, group=this_group_name,
traits_data=self.trait_data_array, corr_array=self.pca_corr_results,
dataset_samples=temp_dataset.group.all_samples_ordered(),
shared_samples=self.shared_samples_list,
create_time=datetime.datetime.now().strftime("%m%d%H%M%S"))
cache_pca_dataset(redis_conn=get_redis_conn(
), exp_days=60 * 60 * 24 * 30, pca_trait_dict=pca_temp_traits)
self.pca_trait_ids = list(pca_temp_traits.keys())
x_coord, y_coord = generate_scree_plot_data(
list(self.pca_obj.explained_variance_ratio_))
self.scree_data = {
"x_coord": x_coord,
"y_coord": y_coord
}
return pca
def export_corr_matrix(corr_results):
corr_matrix_filename = "corr_matrix_" + \
''.join(random.choice(string.ascii_uppercase + string.digits)
for _ in range(6))
matrix_export_path = "{}{}.csv".format(
GENERATED_TEXT_DIR, corr_matrix_filename)
with open(matrix_export_path, "w+") as output_file:
output_file.write(
"Time/Date: " + datetime.datetime.now().strftime("%x / %X") + "\n")
output_file.write("\n")
output_file.write("Correlation ")
for i, item in enumerate(corr_results[0]):
output_file.write("Trait" + str(i + 1) + ": " +
str(item[0].dataset.name) + "::" + str(item[0].name) + "\t")
output_file.write("\n")
for i, row in enumerate(corr_results):
output_file.write("Trait" + str(i + 1) + ": " +
str(row[0][0].dataset.name) + "::" + str(row[0][0].name) + "\t")
for item in row:
output_file.write(str(item[1]) + "\t")
output_file.write("\n")
output_file.write("\n")
output_file.write("\n")
output_file.write("N ")
for i, item in enumerate(corr_results[0]):
output_file.write("Trait" + str(i) + ": " +
str(item[0].dataset.name) + "::" + str(item[0].name) + "\t")
output_file.write("\n")
for i, row in enumerate(corr_results):
output_file.write("Trait" + str(i) + ": " +
str(row[0][0].dataset.name) + "::" + str(row[0][0].name) + "\t")
for item in row:
output_file.write(str(item[2]) + "\t")
output_file.write("\n")
return corr_matrix_filename, matrix_export_path
|
