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}")
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