-
Notifications
You must be signed in to change notification settings - Fork 8
/
PathCNN_GradCAM_modeling.py
203 lines (164 loc) · 6.04 KB
/
PathCNN_GradCAM_modeling.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
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Input
from keras import backend as K
import pandas as pd
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.utils import class_weight
#from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
from keras import regularizers
from keras.layers.core import *
from keras.models import Model
from keras.optimizers import Adam
from sklearn.metrics import confusion_matrix
pca_exp = pd.read_excel("data/PCA_EXP.xlsx", header=None)
pca_cnv = pd.read_excel("data/PCA_CNV.xlsx", header=None)
pca_mt = pd.read_excel("data/PCA_MT.xlsx", header=None)
clinical = pd.read_excel("data/Clinical.xlsx")
n = len(pca_exp) # sample size: number of Pts
path_n = 146 # number of pathways
pc = 5 # number of PCs
# data creation-EXP
pca_exp = pca_exp.to_numpy()
exp_data = np.zeros((n, path_n, pc))
for i in range(n):
for j in range(path_n):
exp_data[i, j, :] = pca_exp[i, j * pc:(j + 1) * pc]
# data creation-CNV
pca_cnv = pca_cnv.to_numpy()
cnv_data = np.zeros((n, path_n, pc))
for i in range(n):
for j in range(path_n):
cnv_data[i, j, :] = pca_cnv[i, j * pc:(j + 1) * pc]
# data creation-MT
pca_mt = pca_mt.to_numpy()
mt_data = np.zeros((n, path_n, pc))
for i in range(n):
for j in range(path_n):
mt_data[i, j, :] = pca_mt[i, j * pc:(j + 1) * pc]
# data merge: mRNA expression, CNV, and MT with a specific number of PCs
no_pc = 2 # use the first 2PCs among 5 PCs
all_data = np.zeros((n, path_n, no_pc * 3))
for i in range(n):
all_data[i, :, :] = np.concatenate((exp_data[i, :, 0:no_pc], cnv_data[i, :, 0:no_pc], mt_data[i, :, 0:no_pc]),axis=1)
clinical = clinical.to_numpy()
age = clinical[:, 4]
survival = clinical[:, 5]
os_months = clinical[:, 6]
idx0 = np.where((survival == 1) & (os_months <= 24))
idx1 = np.where(os_months > 24)
bio_data=clinical[:, 7:10]
all_data = all_data[:, :, :]
data_0 = all_data[idx0, :, :]
data_0 = data_0[0, :, :, :]
data_1 = all_data[idx1, :, :]
data_1 = data_1[0, :, :, :]
age_0 = age[idx0]
age_1 = age[idx1]
bio_data_0 = bio_data[idx0, : ]
bio_data_0 = bio_data_0[0, :, :]
bio_data_1 = bio_data[idx1,:]
bio_data_1 = bio_data_1[0, :, :]
outcomes_0 = np.zeros(len(idx0[0]))
outcomes_1 = np.ones(len(idx1[0]))
## data merge
age_r = np.concatenate((age_0, age_1))
data = np.concatenate((data_0, data_1))
outcomes = np.concatenate((outcomes_0, outcomes_1))
bio_data_r=np.concatenate((bio_data_0, bio_data_1))
## DEEP LEARNING
batch_size = 64
epochs = 30
num_classes = 2
num_runs = 30
k_fold = 5
# input image dimension
img_rows, img_cols = 146, 6
# use all data for modeling
train1 = train = range(data.shape[0])
test = range(data.shape[0])
validation = range(data.shape[0])
x_train = data[train, :, :]
y_train = outcomes[train]
x_validation = data[validation, :, :]
y_validation = outcomes[validation]
x_test = data[test, :, :]
y_test = outcomes[test]
# age
age_r1=age_r[train1]
age_train = age_r1[train]
age_validation = age_r1[validation]
age_test = age_r[test]
# bio
bio_data_r1 = bio_data_r[train1, :]
bio_data_train = bio_data_r1[train,:]
bio_data_validation = bio_data_r1[validation, :]
bio_data_test = bio_data_r[test,:]
cli_train=np.concatenate((np.matrix([age_train]).T, bio_data_train), axis=1)
cli_validation = np.concatenate((np.matrix([age_validation]).T, bio_data_validation), axis=1)
cli_test = np.concatenate((np.matrix([age_test]).T, bio_data_test), axis=1)
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) # rgb- add one more dimensionality
x_validation = x_validation.reshape(x_validation.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
x_train = x_train.astype('float32')
x_validation = x_validation.astype('float32')
x_test = x_test.astype('float32')
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_validation.shape[0], 'validation samples')
print(x_test.shape[0], 'test samples')
y_train = keras.utils.to_categorical(y_train, num_classes)
y_validation = keras.utils.to_categorical(y_validation, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
image_input = Input(shape=input_shape)
other_data_input = Input(shape=(1,))
# First convolution
conv1 = Conv2D(32, kernel_size=(3, 3),
activation='relu', padding='same'
)(image_input)
# Second Convolution
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same'
)(conv1)
conv2 = MaxPooling2D(pool_size=(4, 2))(conv2)
conv2 = Dropout(0.25)(conv2)
first_part_output = Flatten()(conv2)
#merged_model = keras.layers.concatenate([first_part_output, other_data_input])
# without age
merged_model = first_part_output
merged_model = Dense(64, activation='relu')(merged_model)
merged_model = Dropout(0.5)(merged_model)
predictions = Dense(num_classes, activation='softmax')(merged_model)
# Now create the model
model = Model(inputs=[image_input, other_data_input], outputs=predictions)
model.summary()
layers = model.layers
lr = 0.0001
beta_1 = 0.9
beta_2 = 0.999
optimizer = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2)
model.compile(optimizer=optimizer, loss='binary_crossentropy',
metrics=['accuracy'])
class_weighting = {0: 1, 1: 4.2} # class weight
model.fit([x_train, age_train], [y_train],
batch_size=batch_size,
epochs=epochs,
verbose=1, class_weight=class_weighting,
validation_data=([x_validation, age_validation], [y_validation]))
score = model.predict([x_test, age_test], verbose=0)
model.save('PathCNN_model.h5')
observed = y_test
predicted = score
observed = y_test
predicted = score
auc = roc_auc_score(observed[:,0], predicted[:,0])
tn, fp, fn, tp = confusion_matrix(observed[:,0]>0.5, predicted[:,0]>0.5).ravel()
sen = tn/(tn+fp)
spe = tp/(tp+fn)
acc = (tp+tn)/(tp+tn+fp+fn)
print(f"AUC Sen. Spe. Acc.\n{auc:0.2f} {sen:0.2f} {spe:0.2f} {acc:0.2f}")