LogBTFs (Embedding logistic regression into Boolean threshold functions to reconstruct Boolean threshold network)

In this work, an **embedded Boolean threshold network model by aggregating logistic regression with Boolean threshold functions (LogBTFs) ** for inferring gene regulatory networks from single-cell gene expression data was proposed.

LogBTFs

• ** LogBTFs **: A ** embedded Boolean threshold network model (LogBTFs) ** is proposed to infer gene regulatory networks.
• 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 and four embedded regularized support vector machine (Reg-SVM) alternatives.
• 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, et al. "** LogBTFs: Gene regulatory network inference using Boolean threshold networks model from single-cell gene expression data**." Submit to Expert Systems with Applications.

Data

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

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 -- First process the data to get the data of all samples. Then select 112 Tumor + 112 Normal samples to 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 -- Set random seed and output feature selection result of four embedded Lasso-SVM, Enet-SVM, SCAD-SVM, L2SCAD-SVM.
• (9) penaltizedSVMtestNew.R -- use 5-fold cross-validation, 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, a feature selection method for filter, output feature, AUV and pred.
• (12) svmpenalizedmRMR.R -- a filter method, 5-fold cross-validation, select a specified number of features, set feature num = 30.
• (13) svmRFE.R -- The feature selection that implements the SVM-RFE method for 20 experiments, it depends on the following two functions.
• (14) svmpenalizedsvmRFE.R -- Feature selection function for SVM-RFE
• (15) svmRFE_once.R -- The feature selection that implements the SVM-RFE method for only one experiment, it depends on the following two functions.
• (16) plotFeaturesmy.R -- Visualize stability functions of features.

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.
• (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.
• (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.
• (10) confusion.m -- Confusion matrix.
• (11) printConMat -- Output confusion matrix.

LogBTFs (2022), Zhi-Ping Liu all rights reserved

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