├── Cox_DCAP.R ├── DAE-DCAP.py ├── Data_preprocessing.R ├── README.md ├── autoencoder_DCAP.py ├── brca_cox.csv ├── brca_cox2.csv ├── brca_multitest.csv ├── brcatest_go.csv ├── km_plot.R ├── uni_cox.R └── workflow.png /Cox_DCAP.R: -------------------------------------------------------------------------------- 1 | library("xgboost") 2 | library("ipred") 3 | library("survival") 4 | library("survivalROC") 5 | library("glmnet") 6 | library('kernlab') 7 | library('plyr') 8 | library('caret') 9 | 10 | setwd('D:/brca') 11 | 12 | kx2=read.csv('data_cox2.csv',row.names= 1) 13 | sdata=read.csv('brca_go.csv',row.names= 1) 14 | data1=t(sdata) 15 | 16 | j=6 17 | set.seed(j) 18 | k=10 19 | folds <- createFolds(as.data.frame(t(kx2)),k) 20 | results<-matrix(nrow = k, ncol=6) 21 | im=matrix(nrow=500,ncol=30) 22 | eva<-matrix(nrow = nrow(kx2), ncol=k) 23 | count=0 24 | for(i in 1:k){ 25 | testset <- kx2[folds[[i]],] 26 | trainset <- kx2[-folds[[i]],] 27 | te_xg=data1[folds[[i]],] 28 | tr_xg=data1[-folds[[i]],] 29 | x=trainset[,-c(1:2)] 30 | x=as.matrix(x) 31 | tc=trainset$time 32 | tc[tc==0]=0.001 33 | y=Surv(tc,trainset$status) 34 | cv.fit<-cv.glmnet(x,y,family="cox",maxit=10000,alpha=0,nfold=5) 35 | fit<-glmnet(x,y,family="cox",alpha=0) 36 | tt=predict(fit,x,s=cv.fit$lambda.min) 37 | x2=testset[,-c(1:2)] 38 | x2=as.matrix(x2) 39 | tc2=testset$time 40 | tc2[tc2==0]=0.001 41 | y2=Surv(tc2,testset$status) 42 | tt2=predict(fit,x2,s=cv.fit$lambda.min) 43 | y_xgtr=tt; 44 | x_xgtr=tr_xg 45 | x_xgtr=as.matrix(x_xgtr) 46 | x_xgte=te_xg 47 | x_xgte=as.matrix(x_xgte) 48 | bst <- xgboost(x_xgtr, y_xgtr, 49 | max_depth =3, eta =0.25, nrounds =6, min_child_weight=2, 50 | objective = "reg:linear",eval_metric = "rmse") 51 | pred <- predict(bst, x_xgte) 52 | eva[(count+1):(count+nrow(tt2)),1]<-tc2 53 | eva[(count+1):(count+nrow(tt2)),2]<-testset$status 54 | eva[(count+1):(count+nrow(tt2)),3]=tt2 55 | eva[(count+1):(count+nrow(tt2)),4]=pred 56 | count=count+nrow(tt2) 57 | 58 | } 59 | 60 | tc3=eva[,1] 61 | st3=eva[,2] 62 | rk3=eva[,3] 63 | rk4=eva[,4] 64 | 65 | y3=Surv(tc3,st3) 66 | ci_Cox=survConcordance(formula = y3~ rk3) 67 | ci_Cox$concordance 68 | ci_XGB=survConcordance(formula = y3~ rk4) 69 | ci_XGB$concordance 70 | 71 | 72 | -------------------------------------------------------------------------------- /DAE-DCAP.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | import tensorflow as tf 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import pandas as pd 7 | from sklearn.cluster import KMeans 8 | from sklearn.metrics import silhouette_score 9 | 10 | with open(r"simulation.csv", 'r') as f: 11 | data = pd.read_csv(f) 12 | 13 | #print(data.shape) 14 | tcga_input=np.transpose(data) 15 | print(tcga_input.shape[1]) 16 | length1 = tcga_input.shape[1] 17 | learning_rate = 0.0001 18 | training_epochs = 100 19 | batch_size = 125 20 | display_step = 2 21 | examples_to_show = 10 22 | n_input = tcga_input.shape[1] 23 | 24 | 25 | X = tf.placeholder("float", [None, n_input]) 26 | 27 | 28 | n_hidden_1 = 200 29 | n_hidden_2 = 50 30 | n_hidden_3 = 2 31 | 32 | weights = { 33 | 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 34 | 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 35 | 'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])), 36 | 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])), 37 | 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), 38 | 'decoder_h3': tf.Variable(tf.random_normal([n_hidden_1, n_input])), 39 | 40 | 41 | } 42 | biases = { 43 | 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 44 | 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), 45 | 'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])), 46 | 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_2])), 47 | 'decoder_b2': tf.Variable(tf.random_normal([n_hidden_1])), 48 | 'decoder_b3': tf.Variable(tf.random_normal([n_input])), 49 | } 50 | 51 | 52 | def encoder(x): 53 | layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['encoder_h1']), 54 | biases['encoder_b1'])) 55 | layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['encoder_h2']), 56 | biases['encoder_b2'])) 57 | layer_3 = tf.nn.tanh(tf.add(tf.matmul(layer_2, weights['encoder_h3']), 58 | biases['encoder_b3'])) 59 | return layer_3 60 | 61 | 62 | 63 | def decoder(x): 64 | layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['decoder_h1']), 65 | biases['decoder_b1'])) 66 | layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['decoder_h2']), 67 | biases['decoder_b2'])) 68 | layer_3 = tf.nn.tanh(tf.add(tf.matmul(layer_2, weights['decoder_h3']), 69 | biases['decoder_b3'])) 70 | return layer_3 71 | 72 | 73 | encoder_op = encoder(X) 74 | decoder_op = decoder(encoder_op) 75 | 76 | 77 | y_pred = decoder_op 78 | y_true = X 79 | 80 | 81 | cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) 82 | optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) 83 | 84 | with tf.Session() as sess: 85 | if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: 86 | init = tf.initialize_all_variables() 87 | else: 88 | init = tf.global_variables_initializer() 89 | sess.run(init) 90 | 91 | total_batch = int(len(tcga_input)/batch_size) 92 | for epoch in range(training_epochs): 93 | for i in range(total_batch): 94 | batch_xs = tcga_input[((i) * batch_size):((i + 1) * batch_size)] + 0.3 * np.random.rand(length1) #added nosie 95 | # Run optimization op (backprop) and cost op (to get loss value) 96 | _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) 97 | if epoch % display_step == 0: 98 | print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c)) 99 | if epoch == training_epochs - 1: 100 | fea_output = sess.run([encoder_op], feed_dict={X: tcga_input}) 101 | # print(fea_output) 102 | print(np.array(fea_output).shape) 103 | np.savetxt(r'fea.csv', np.array(fea_output[0]), delimiter=',') 104 | dd = np.array(fea_output[0]) 105 | print("Optimization Finished!") 106 | print(dd.shape) 107 | clf = KMeans(n_clusters=2) 108 | clf.fit(dd) 109 | centers = clf.cluster_centers_ 110 | labels = clf.labels_ 111 | silhouetteScore = silhouette_score(dd, labels, metric='euclidean') 112 | print(centers) 113 | print(silhouetteScore) 114 | # encode_decode = sess.run( 115 | # y_pred, feed_dict={X: mnist.test.images[:examples_to_show]}) 116 | # f, a = plt.subplots(2, 10, figsize=(10, 2)) #return fig,axes 117 | # for i in range(examples_to_show): 118 | # a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28))) 119 | # a[1][i].imshow(np.reshape(encode_decode[i], (28, 28))) 120 | # plt.show() -------------------------------------------------------------------------------- /Data_preprocessing.R: -------------------------------------------------------------------------------- 1 | setwd('D:/brca') 2 | library('imputeMissings') 3 | data=read.csv("brca_multitest.csv",row.names = 1) 4 | 5 | #################delete missing####################### 6 | sum(is.na(data)) 7 | miss=c() 8 | for (i in 1:nrow(data)){ 9 | miss=c(miss,sum(is.na(data[i,]))) 10 | } 11 | missrate=miss/ncol(data) 12 | data1=data[missrate<0.2,] 13 | data1[is.na(data1)] <- 0 14 | ###################delete zero################# 15 | nz=c() 16 | for (i in 1:nrow(data1)){ 17 | nz=c(nz,sum(data1[i,]==0)) 18 | } 19 | zerorate=nz/ncol(data1) 20 | data2=data1[zerorate<0.2,] 21 | ###############impute###################### 22 | 23 | data3=t(data2) 24 | data3=data.frame(data3) 25 | data3[data3==0]=NA 26 | data4<-impute(data3) 27 | ##########normalize############## 28 | data5=t(data4) 29 | data6=data5 30 | data7=t(data6) 31 | data8=scale(data7, center = T, scale = T) 32 | data9=t(data8) 33 | write.csv(data9,'brcatest_go.csv') 34 | 35 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DCAP 2 | DCAP:Integrating multi-omics data with deep learning for predicting cancer prognosis 3 | DCAP is designed to integrate multi-omics data for predicting the prognosis risk of cancer patients.The multi-omics data was input into Autoencoder to obtain representative composite features that can best rebuild all input data. These features were then input to the Cox proportional hazards (Cox-PH) model to estimate the patients’ prognosis risks. Finally, in order to reduce the number of features for cancer prognosis prediction, we employed XGboost for features selection, and reconstruct the cancer prognosis model. 4 | 5 | # Requirements 6 | tensorflow, python 3, R 7 | 8 | # Data preparation 9 | In this study, we utilized cancer datasets from the TCGA portal (https://tcga-data.nci.nih.gov/tcga/). All these datasets were downloaded by using the R package “TCGA-assembler”(v1.0.3, (Wei, et al., 2018)), which contains four types of multi-omics data: mRNA, miRNA, DNA methylation, and copy number variation (CNV) data. Here, “mRNA” was RNA sequencing data generated by UNC Illumina HiSeq_RNASeq V2; Level 3, “miRNA” was miRNA sequencing data obtained by BCGSC Illumina HiSeq miRNASeq, DNA methylation data was generated by USC HumanMethylation450, and CNV data that generated by BROAD-MIT Genome wide SNP_6. 10 | 11 | # Usage 12 | DCAP was a framework with three different method. Firstly please use the autoencoder_DCAP.py to obtain representative composite features that can best rebuild all input multi-omics data. The table named fea.csv will be saved in the file folder. After that a table (Such as brca_cox.csv) should be constructed by users, this table will be put into uni_cox.R (), and the generated file (such as brca_cox2.csv) will be processed by Cox_DCAP for getting the finally results. The CI_Cox$concordance is the C-index value obtained by DCAP_Cox and the CI-XGB is the C-index value obtained by XGBoost. (If users need data preprocessing, the Data_preprocessing.R can be used) 13 | 14 | Beforing using please checking the file folder path. 15 | 16 | # Example 17 | For easy to use, here we give four example data: brca_multitest.csv used for Data_preprocessing.R, brcatest_go.csv used for autoencoder_DCAP.py, brca_cox.csv used for uni_cox.R and brca_cox2.csv used for Cox_DCAP. Due to file size limitation, we only uploaded brca-omics data (brcatest_go.csv for 10 patients with 10 genes) as an example. Users can build data as the format of the example. 18 | 19 | # Contact 20 | This method is still on progress, any questions can be sent to chaihuachaihua@qq.com 21 | -------------------------------------------------------------------------------- /autoencoder_DCAP.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | import tensorflow as tf 4 | import numpy as np 5 | import matplotlib.pyplot as plt 6 | import pandas as pd 7 | with open(r"C:\pypro\brcatest_go.csv", 'r') as f: 8 | data = pd.read_csv(f) 9 | 10 | print(data.shape) 11 | tcga_input=np.transpose(data) 12 | print(tcga_input.shape) 13 | 14 | learning_rate = 0.01 15 | training_epochs = 10 16 | batch_size = 50 17 | display_step = 1 18 | examples_to_show = 10 19 | 20 | dropout=0.1 21 | n_input = 60779 22 | scale = 0.0001 23 | # tf Graph input (only pictures) 24 | X = tf.placeholder("float", [None, n_input]) 25 | 26 | 27 | n_hidden_1 = 500 # 28 | n_hidden_2 = 200 # 29 | 30 | weights = { 31 | 'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 32 | 'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 33 | 'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])), 34 | 'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])), 35 | } 36 | biases = { 37 | 'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 38 | 'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])), 39 | 'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])), 40 | 'decoder_b2': tf.Variable(tf.random_normal([n_input])), 41 | } 42 | 43 | 44 | def encoder(x): 45 | layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['encoder_h1']), 46 | biases['encoder_b1'])) 47 | layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['encoder_h2']), 48 | biases['encoder_b2'])) 49 | return layer_2 50 | 51 | 52 | 53 | def decoder(x): 54 | layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['decoder_h1']), 55 | biases['decoder_b1'])) 56 | layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['decoder_h2']), 57 | biases['decoder_b2'])) 58 | return layer_2 59 | 60 | 61 | ################################################################## 62 | 63 | fc_1 = tf.layers.dense(inputs=X, units=n_hidden_1, 64 | kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=scale)) 65 | fc_1_out = tf.nn.tanh(fc_1) 66 | fc_1_dropout = tf.layers.dropout(inputs=fc_1_out, rate=dropout) 67 | 68 | fc_2 = tf.layers.dense(inputs = fc_1_dropout, units = n_hidden_2, kernel_regularizer= tf.contrib.layers.l2_regularizer(scale=scale)) 69 | fc_2_out = tf.nn.tanh(fc_2) 70 | encoder_op = tf.layers.dropout(inputs=fc_2_out, rate=dropout) 71 | 72 | fc_3 = tf.layers.dense(inputs = encoder_op, units = n_hidden_1, kernel_regularizer= tf.contrib.layers.l2_regularizer(scale=scale)) 73 | fc_3_out = tf.nn.tanh(fc_3) 74 | fc_3_dropout = tf.layers.dropout(inputs=fc_3_out, rate=dropout) 75 | 76 | decoder_op = tf.layers.dense(inputs=fc_3_dropout, units=n_input) 77 | ################################################################## 78 | 79 | 80 | y_pred = decoder_op 81 | y_true = X 82 | 83 | 84 | cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))#+lossL 85 | l2_loss = tf.losses.get_regularization_loss() 86 | optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost+l2_loss) 87 | 88 | with tf.Session() as sess: 89 | # tf.initialize_all_variables() no long valid from 90 | # 2017-03-02 if using tensorflow >= 0.12 91 | if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: 92 | init = tf.initialize_all_variables() 93 | else: 94 | init = tf.global_variables_initializer() 95 | sess.run(init) 96 | 97 | total_batch = int(len(tcga_input)/batch_size) 98 | for epoch in range(training_epochs): 99 | for i in range(total_batch): 100 | # tch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x) = 1, min(x) = 0 101 | batch_xs = tcga_input[((i)*batch_size):((i+1)*batch_size)] 102 | # Run optimization op (backprop) and cost op (to get loss value) 103 | _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs}) 104 | if epoch % display_step == 0: 105 | print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c)) 106 | if epoch == training_epochs - 1: 107 | fea_output = sess.run([encoder_op], feed_dict={X: tcga_input}) 108 | # print(fea_output) 109 | print(np.array(fea_output).shape) 110 | np.savetxt(r'C:\pypro\fea.csv', np.array(fea_output[0]), delimiter=',') 111 | print("Optimization Finished!") 112 | 113 | 114 | -------------------------------------------------------------------------------- /brca_cox2.csv: -------------------------------------------------------------------------------- 1 | "","time","status","X.1","X.7","X.10","X.13","X.14","X.17","X.21","X.22","X.32","X.44","X.45","X.62","X.73","X.83","X.109","X.117","X.125","X.144","X.148","X.168","X.195","X.197" 2 | 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-------------------------------------------------------------------------------- /brca_multitest.csv: -------------------------------------------------------------------------------- 1 | GeneSymbol,TCGA-3C-AAAU-01,TCGA-3C-AALI-01,TCGA-3C-AALJ-01,TCGA-3C-AALK-01,TCGA-4H-AAAK-01,TCGA-5L-AAT0-01,TCGA-5T-A9QA-01,TCGA-A1-A0SB-01,TCGA-A1-A0SF-01,TCGA-A1-A0SG-01 2 | A1BGcopyNumber,0.1513,0.2507,0.038,-0.0173,0.2094,0.0201,-0.064,0.0143,0.1805,-0.007 3 | A1BGgeneExp,197.0897,237.3844,423.2366,191.0178,268.8809,203.7718,314.1841,49.1992,326.0194,168.8309 4 | A1BGmethylation,0.643079608,0.880189069,0.84142049,0.821475582,0.828328828,0.638135542,0.888757923,0.89579653,0.7369306,0.860262636 5 | A1BG-AS1copyNumber,0.1513,0.2507,0.038,-0.0173,0.2094,0.0201,-0.064,0.0143,0.1805,-0.007 6 | A1BG-AS1geneExp,147.5553,288.8635,197.5521,139.2511,205.9447,160.4517,118.6611,75.0039,187.298,148.6599 7 | A1BG-AS1methylation,0.643079608,0.880189069,0.84142049,0.821475582,0.828328828,0.638135542,0.888757923,0.89579653,0.7369306,0.860262636 8 | A1CFcopyNumber,-0.2015,-0.3155,0.0661,0.0031,0.0112,-0.0059,0.3714,0.016,0.2051,-0.0011 9 | A1CFgeneExp,0,0,0.9066,0,0.4255,0,0.523,0,0,0 10 | A1CFmethylation,,0.486560435,,0.489612445,,,0.665746039,0.632599688,0.513556682,0.442433766 11 | A2McopyNumber,0.2061,0.0322,0.054,-0.01,-0.2571,-0.4616,-0.0211,0.0151,0.2312,0.0259 12 | A2MgeneExp,5798.3746,7571.9793,8840.3989,10960.2193,9585.4426,12331.3213,3182.4843,31626.949,15583.9901,12472.9871 13 | A2Mmethylation,0.704462294,0.084947404,0.428228408,0.577717061,0.553690674,0.199388879,0.577891245,0.199684815,0.126475723,0.092899668 14 | A2ML1copyNumber,0.2061,0.0322,0.054,-0.01,-0.2571,-0.4616,-0.0211,0.0151,0.2312,0.0259 15 | A2ML1geneExp,1.3786,4.3502,0,1.6549,3.4043,1.1732,0,3.1535,3.4272,1.8 16 | A2ML1methylation,0.633692137,0.685413676,0.643626892,0.67602514,0.648158701,0.717025857,0.684029124,0.549694851,0.608472025,0.659739472 17 | A4GALTcopyNumber,-0.3746,-0.3984,-0.5817,0.0057,-0.0111,0.0139,0.5627,0.0191,-0.0423,-0.174 18 | A4GALTgeneExp,68.2424,157.6944,573.8894,506.4129,342.1277,370.1422,411.6109,337.4254,441.2557,619.5662 19 | A4GALTmethylation,0.043635038,0.052533493,0.076322008,0.109997618,0.044317172,0.058199021,0.037480155,0.031044178,0.053004997,0.038472652 20 | A4GNTcopyNumber,0.3052,0.3422,-0.0555,-0.0112,0.0181,0.0022,0.0351,0.0168,-0.027,-0.004 21 | A4GNTgeneExp,8.6165,0.5438,0,0,0.4255,4.1062,3.6611,0.901,0.8568,0.72 22 | A4GNTmethylation,0.416098582,0.652867059,0.470022998,0.353603261,0.453930906,0.500432613,0.625209334,0.905906995,0.583524561,0.368256527 23 | AAAScopyNumber,-0.2881,0.0327,-0.2098,-0.0122,-7.00E-04,-0.0031,0.0152,0.0148,0.2267,0.0107 24 | AAASgeneExp,649.3372,887.9826,723.4814,776.9963,819.1489,954.3921,873.431,711.7919,1005.4632,718.9272 25 | AAASmethylation,0.153639259,0.056005284,0.064451814,0.06474162,0.063210385,0.065299265,0.245827732,0.044087852,0.049161408,0.054861158 26 | AACScopyNumber,-0.2949,0.0235,0.2808,-0.0021,0.0078,0.0166,0.0175,0.018,-0.3375,0.0107 27 | AACSgeneExp,1087.3986,836.3241,1533.0916,858.0885,840,833.5533,1198.2218,2184.0297,442.9693,1721.1772 28 | AACSmethylation,0.039804689,0.032788507,0.036785039,0.031540863,0.035812584,0.033641279,0.044470263,0.026614335,0.024538091,0.021294651 29 | AACSP1copyNumber,0.2047,0.0055,0.0607,0.0075,0.0164,0.0042,-0.003,0.0134,-0.0359,0.0036 30 | AACSP1geneExp,1.3786,35.3453,2.7199,0,0.4255,0,0,820.3627,0,0 31 | AACSP1methylation,0.839495519,0.809946531,0.786439135,0.805374771,0.83934796,0.804572447,0.867673107,0.865306083,0.816790234,0.783979981 32 | -------------------------------------------------------------------------------- /brcatest_go.csv: -------------------------------------------------------------------------------- 1 | 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-1.539610917,0.283145519,1.756120491,0.136592238,0.19326714,0.24364483,0.248797094,0.251659463,-1.783484737,0.209868879 23 | -0.127234437,-0.610618681,0.730841448,-0.568716504,-0.60354161,-0.615953197,0.086129281,1.984067965,-1.367929808,1.092955544 24 | 0.999146699,0.008398673,0.57274495,-0.167779885,0.435425555,0.128817753,1.657967726,-0.863449965,-1.156634298,-1.614637209 25 | 2.662463249,-0.334105366,0.496269069,-0.304019336,-0.170136501,-0.353661286,-0.461970995,-0.215265547,-0.956886193,-0.362687095 26 | 0.589279211,-0.399907945,-1.186845698,-0.552952993,0.5843395,-0.579811733,1.532557139,1.453318224,-0.170806931,-1.269168773 27 | -------------------------------------------------------------------------------- /km_plot.R: -------------------------------------------------------------------------------- 1 | library("ipred") 2 | library("survival") 3 | library("survivalROC") 4 | library("glmnet") 5 | library('kernlab') 6 | library('caret') 7 | setwd('D:/pan-cancer/brca') 8 | par(mfrow = c(3, 4)) 9 | ########################LIHC######################### 10 | kx2=read.csv('brca_cox2.csv',row.names= 1) 11 | ind=5 12 | results_fin<-matrix(nrow = ind, ncol=6) 13 | j=6 14 | set.seed(j) 15 | k=10 16 | folds <- createFolds(as.data.frame(t(kx2)),k) 17 | results<-matrix(nrow = k, ncol=4) 18 | eva<-matrix(nrow = nrow(kx2), ncol=k) 19 | count=0 20 | for(i in 1:k){ 21 | testset <- kx2[folds[[i]],] 22 | trainset <- kx2[-folds[[i]],] 23 | x=trainset[,-c(1:2)] 24 | x=as.matrix(x) 25 | tc=trainset$time 26 | tc[tc==0]=0.001 27 | y=Surv(tc,trainset$status) 28 | cv.fit<-cv.glmnet(x,y,family="cox",maxit=10000,alpha=0,nfold=5) 29 | fit<-glmnet(x,y,family="cox",alpha=0) 30 | tt=predict(fit,x,s=cv.fit$lambda.min) 31 | x2=testset[,-c(1:2)] 32 | x2=as.matrix(x2) 33 | tc2=testset$time 34 | tc2[tc2==0]=0.001 35 | y2=Surv(tc2,testset$status) 36 | tt2=predict(fit,x2,s=cv.fit$lambda.min) 37 | ci_k2=survConcordance(formula = y2~ tt2) 38 | 39 | 40 | results[i,1]<-ci_k2$concordance 41 | eva[(count+1):(count+nrow(tt2)),1]<-tc2 42 | eva[(count+1):(count+nrow(tt2)),2]<-testset$status 43 | eva[(count+1):(count+nrow(tt2)),3]=tt2 44 | count=count+nrow(tt2) 45 | 46 | } 47 | 48 | tc3=eva[,1]/365 49 | st3=eva[,2] 50 | rk3=eva[,3] 51 | rk4=eva[,4] 52 | y3=Surv(tc3,st3) 53 | ci_k3=survConcordance(formula = y3~ rk3) 54 | ci_k3$concordance 55 | mm=median(rk3) 56 | for (p in 1:length(rk3)){ 57 | if (rk3[p]>mm){ 58 | rk3[p]=1 59 | } else 60 | {rk3[p]=-1} 61 | } 62 | q2=survdiff(y3~rk3) 63 | p.val <- 1 - pchisq(q2$chisq, length(q2$n) - 1) 64 | p.val 65 | sd11=survfit(y3~rk3) 66 | 67 | 68 | 69 | 70 | ################################################ 71 | kt2=kx2[,-(1:2)] 72 | cl <- kmeans(kt2, 2) 73 | riskk=cl$cluster 74 | yy=Surv((kx2$time)/365,kx2$status) 75 | sp=survdiff(yy~riskk) 76 | p.valk2 <- 1 - pchisq(sp$chisq, length(sp$n) - 1) 77 | p.valk2 78 | riskk[riskk==2]=0 79 | #riskk_bl=riskk 80 | sd12=survfit(yy~riskk_bl) 81 | 82 | 83 | plot(sd11,conf.int=FALSE,mark.time=T,col=c("green","red"),lty=1,main="BRCA", 84 | cex.main=1.5,xlab="Year",ylab = "Survival Probablity",cex.lab=1.4,cex.axis=1.2,lwd=2) 85 | lines(sd12,mark.time=T,col=c("grey","grey"),lty=2,lwd=2) 86 | legend('topright',c('DCAP-low risk','DCAP-high risk'),lty=c(1,1),col=c(3,2),cex=1.2) -------------------------------------------------------------------------------- /uni_cox.R: -------------------------------------------------------------------------------- 1 | setwd('D:/brca') 2 | library('plyr') 3 | library("ipred") 4 | library("survival") 5 | library("survivalROC") 6 | library("glmnet") 7 | kx=read.csv("brca_cox.csv",row.names= 1) 8 | mysurv=Surv(kx$time,kx$status) 9 | 10 | 11 | Unicox <- function(x){ 12 | #cat("aa:",x,"\n") 13 | fml <- as.formula(paste0('mysurv~', x)) 14 | gcox <- coxph(fml, kx) 15 | cox_sum <- summary(gcox) 16 | HR <- round(cox_sum$coefficients[,2],2) 17 | PValue <- round(cox_sum$coefficients[,5],4) 18 | CI <- paste0(round(cox_sum$conf.int[,3:4],2),collapse='-') 19 | Uni_cox <- data.frame('Characteristics' = x, 20 | 'Hazard Ratio' = HR, 21 | 'CI95' = CI, 22 | 'P value' = PValue) 23 | return(Uni_cox) 24 | } 25 | VarNames <-colnames(kx)[3:ncol(kx)] 26 | Univar <- lapply(VarNames, Unicox) 27 | Univar <- ldply(Univar, data.frame) 28 | 29 | Univar[,5]=p.adjust(Univar$P.value, method ="fdr", n=dim(Univar)[1])# 30 | colnames(Univar)<-c("Characteristics", "Hazard.Ratio", "CI95", "P.value", "adj.p") 31 | dd=matrix(0,1,ncol(kx)) 32 | dd[1,3:ncol(kx)]=Univar$P.value 33 | dd=data.frame(dd) 34 | colnames(dd)=colnames(kx) 35 | ee=rbind(dd,kx) 36 | ff=ee[,ee[1,]<0.05] 37 | gg=ee[,ee[1,]<0.01] 38 | cox2=ff[-1,] 39 | write.csv(cox2,'brca_cox2.csv') 40 | -------------------------------------------------------------------------------- /workflow.png: -------------------------------------------------------------------------------- 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