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CNet-SVM: Biomarker discovery from high-throughput data by connected network-constrained support vector machine

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In this work, a connected network-constrained support vector machine (CNet-SVM) model for feature selection considering the structural connectivity in a network was proposed. Mathematically, it was a convex optimization problem constrained by inequalities reflecting network connectivity.

CNet

  • CNet: A connected network-constrained support vector machine (CNet-SVM) model is proposed to perform feature selection.
  • In both synthetic simulation data and real-world breast cancer (BRCA) genomics data, we validated the CNet-SVM model is efficient to identify the connected-network-structured features that can serve as diagnostic biomarkers.
  • In the comparison study, we also proved the proposed CNet-SVM model results in better classification performance and feature interpretability than one wrapped method named SVM-RFE, one filter method named mRMR-SVM, four embedded regularized support vector machine (Reg-SVM) alternatives: Lasso-SVM, ENet-SVM, SCAD-SVM and L2DCAD-SVM and two gtoup structural SVM methods: GLasso-SVM and SGLasso-SVM.
  • If you have any questions about CNet-SVM, please directly contact the corresponding author Prof. Zhi-Ping Liu with the E-mail: [email protected]

Citation

Li, Lingyu, and Zhi-Ping Liu. "Biomarker discovery from high-dimensional omics data by connected network-constrained support vector machine." Accepted by Expert Systems with Applications.

Data

  • In the CNet-SVM, NSLR_example and matlab_example files, we give all R/Matlab/Python codes.
  • In the Data file, we give some necessary input/output files by the R/Matlab/Python codes.
  • Some input files only give the first few lines, but this does not affect the results of the work (CNet-SVM).
  • In the Supplementary file file, we present the necessary Additional files contained in our work.

R packages

R codes for Data (RforData)

The serial number (1), (2), ..., (16) represents the order in which the program runs in our work.

  • (1) TCGA_pro_clin_DE.R -- Get data of all samples, select 112 Tumor + 112 Normal samples to and get DEGs.
  • (2) thetaSelectGEDFN.R -- Use GCWs method get top 1% genes, repeat 10 times, make union.
  • (3) malacards_GEDFN_mamaprint_KEGG.R -- Integrate data from MalaCards, KEGG, Mamaprint, GCWs, DEGs to union gene and corresponding expression data.
  • (4) network_match_union.R -- Get the network of union gene in RegNetwork, extract the expression data of TCGA corresponding to union gene, and scale them.
  • (5) data_splitnew.R -- According to the random seeds of other methods, the scaled data of the union gene of TCGA is divided into training data and testing data.
  • (6) adj_union.R ---- Adjacency matrix and its eigenvalues.
  • (7) cut_union.R ---- Diameters and cut-nodes of component of DEGs in RegNetwork.
  • (8) svmpenalized.R -- Output feature selection results of Lasso-SVM, Enet-SVM, SCAD-SVM and L2SCAD-SVM. 
    To test four embedded Reg-SVM methods, simply uses the function 'svmpenalized.R'.
  • (9) penaltizedSVMtestNew.R -- Use 5-fold cross-validation (CV), embedding function svmpenalized.R.
  • (10) penaltizedSVMresultOnce.R -- Results for feature selection and classification on train data and test set.
  • (11) filtermRMR.R -- Use svmpenalizedmRMR.R, output feature, AUC and pred.
  • (12) svmpenalizedmRMR.R -- A filter method, select a specified number of features, set feature num = 30.
  • (13) svmRFE.R -- The SVM-RFE method for 20 experiments.
  • (14) svmpenalizedsvmRFE.R -- Feature selection function for SVM-RFE.
  • (15) svmRFE_once.R -- The SVM-RFE method for one experiment.
  • (16) plotFeaturesmy.R -- Visualize stability functions of features.
  • (17*) GL-HSVMonce.R -- Newly add GLasso-SVM method to compare with CNet-SVM.
  • (18*) SGL-SVMonce -- Newly add SGL-SVM method to compare with CNet-SVM.
  • (19*) TCGA_pro_clin_DE_R3.R -- It accesses the whole data of breast cancer, 1205 samples.
  • (20*) SGL-SVMonce -- Newly add SGL-SVM method to compare with CNet-SVM.
  • (21*) rfe_nnet_R3 -- Newly add Neural Network (NNet) method to compare with CNet-SVM.
  • (22*) heatmap_R3.R -- Followed "TCGA_pro_clin_DE_R3.R", it is used to see the 32 biomarkers's performance on whole 1205 sample of TCGA.

R codes for Result (RforResult)

The serial number (1), (2), ..., (4) represents the order in which the program runs in our work.

  • (1) feature_select_all_new.R -- Extract the common genes of TCGA and GEO, using the identified 32 genes.
  • (2) class_net_svm.R -- Train on TCGA data, predict on GEO data, apply linear svm classifier for classification, observe results.
  • (3) network_match_all_new.R -- Extract the net information of the biomarkers identified by each method.
  • (4) ROCplot.R -- Plot ROC curves on independent datasets.

R codes for simulating datasets (NLSR)

The serial number (1), (2), ..., (9) represents the order in which the program runs in our work.

  • (1) penaltizedSVMexample.R -- Try to generate simulation data, the output format used by R (.txt) is different from that used by matlab (.csv), and saved in the file SVM\R\NSLR
  • (2) A_Simple_Example_svm.R -- Get the feature selection and classification results of SCAD, lasso, elastic net, L2SCAD ExampleData, the results are in the table result_example.csv*
  • (3) coef2feature_examplenew.R -- Extract the threshold according to the coefficient (obtained from matlab) to get the feature.
  • (4) filtermRMRe_example.R -- Feature selection results of mRMR-SVM on simulated datasets.
  • (5) svmRFE_example.R -- Feature selection results of SVM-RFE on simulated datasets.
  • (6) svmpenalizedsvmRFE.R -- Serve svmRFE_example.R
  • (7) svmrfeFeatureRanking.R -- Serves svmRFE_example.R
  • (8) adj_example.R -- Adjacency matrix converted to a list of gene pairs
  • (9) cut_example.R -- INPUT gene and gene net, OUTPUT cut node and cut_vector_UNgene

Matlab codes for simulating datasets (SVMexamplenew)

  • (1) SVMmainexample.m -- main function. 
    To test CNet-SVM method on simulation data, simply uses the script 'matlab -r SVMmainexample.m'.
  • (2) costFunctionSVM.m -- Objective function.
  • (3) cvSVM.m -- Cross validation to select optimal parameters.
  • (4) Laplcian_Matrix.m -- Laplacian matrix according to the adjacency matrix.
  • (5) LogitisLapSVM.m -- LogitisLap function for CV.
  • (6) SGNLR.m -- SGNLR function for LogitisLap.
  • (7) ErrorSVM.m -- Error function.
  • (8) getLambMaxSVM.m -- getLambMax function for cv.
  • (9) PredictSVM.m -- Predict function on test dataset.
  • (10) plotROC.m -- Roc curve function on test dataset.
  • (11) printConMat -- Output confusion matrix.

Matlab codes for breast cancer dataset (SVMnew)

  • (1) SVMmainUnion23.m -- main function. 
    To test CNet-SVM method on real BRCA data, simply uses the script 'matlab -r SVMmainUnion23.m'.
  • (2) costFunctionSVM.m -- Objective function.
  • (3) cvSVM.m -- Cross validation to select optimal parameters.
  • (4) Laplcian_Matrix.m -- Laplacian matrix according to the adjacency matrix.
  • (5) LogitisLapSVM.m -- LogitisLap function for cv.
  • (6) SGNLR.m -- SGNLR function for LogitisLap.
  • (7) ErrorSVM.m -- Error function.
  • (8) getLambMaxSVM.m -- getLambMax function for cv.
  • (9) PredictSVM.m -- Predict function on test dataset.
  • (10) plotROC.m -- Roc curve function on test dataset.
  • (11) confusion.m -- Confusion matrix.
  • (12) printConMat -- Output confusion matrix.

Python codes (GEDFNsvm)

  • (1) main_SVM.py -- GCWs methods for feature selection. 
    To conduct GCWs method, simply use script 'python main_SVM.py'.
  • (2*) rfe_rf_R3.ipynb -- Newly add Random Forest (RF) method to compare with CNet-SVM. 
    To test RF method on whole 1205 BRCA data, simply run 'rfe_rf_R3.ipynb' use jupyter notebook.

CNet (2023), Zhi-Ping Liu all rights reserved

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